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Satellite Remote Sensing 

for Archaeology 

Sarah H. Parcak 


This handbook is the first comprehensive overview of the field of satellite remote sens- 
ing for archaeology and of how it can be applied to ongoing archaelogical fieldwork 
projects across the globe. It provides a survey of the history and development of the 
field, connecting satellite remote sensing, archaeological method and theory, cultural 
resource management, and environmental studies. With a focus on the practical use of 
satellite remote sensing, Sarah H. Parcak evaluates satellite imagery types and remote 
sensing analysis techniques specific to the discovery, preservation, and management of 
archaeological sites. 

Case studies from Asia, Central America, and the Middle East are explored, includ- 
ing Xi'an, China, Angkor Wat, Cambodia, and Egypt's floodplains. In-field surveying 
techniques particular to satellite remote sensing are emphasized, providing strategies for 
recording ancient features on the ground as observed from space. The book also discusses 
broader issues relating to archaeological remote sensing ethics, looting prevention, and 
archaeological site preservation. New sensing research is included and illustrated with 
the inclusion of over 160 satellite images of ancient sites. 

With a companion website with further resources and colour images, Satellite Remote 
Sensing for Archaeology will provide anyone interested in scientific applications to 
uncovering past archaeological landscapes with a foundation for future research and 

Sarah H. Parcak, PhD, is an Assistant Professor of Anthropology at the University of 
Alabama at Birmingham, USA and Director of the University's Laboratory of Global 
Health Observation. She directs the Middle Egypt Survey Project, and is co-Director of 
the Survey and Excavation Projects in Egypt. 




Sarah H. Parcak 

Q Routledge 

S^^ Taylor &. Francis Group 

First published 2009 

by Routledge 

2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN 

Simultaneously published in the USA and Canada 

by Routledge 

270 Madison Ave., New York, NY 10016 

Routledge is an imprint of the Taylor & Francis Group, 
an informa business 

This edition published in the Taylor & Francis e-Library, 2009. 

To purchase your own copy of this or any of Taylor & Francis or Routledge' s 
collection of thousands of eBooks please go to 

© 2009 Sarah H. Parcak 

All rights reserved. No part of this book may be reprinted or reproduced or 

utilized in any form or by any electronic, mechanical, or other means, now 

known or hereafter invented, including photocopying and recording, 

or in any information storage or retrieval system, without permission 

in writing from the publishers. 

British Library Cataloguing in Publication Data 

A catalogue record for this book is available 

from the British Library 

Library of Congress Cataloging in Publication Data 
Parcak, Sarah H. 
Satellite remote sensing for archeaology/Sarah H. Parcak 
p. cm. 
1. Archaeology— Remote sensing. 2. Imaging systems in archaeology. 
3. Remote-sensing images 4. Archaeology-Fieldwork. 5. Excavations 
(Archaeology) 6. Historic sites-Conservation and restoration. 
7. Antiquities-Collection and preservation. 8. Archaeology-Methodology. 
9. Archaeology— Moral and ethical aspects. 10. Archaeologists— Professional 
ethics. I. Title. 
CC76.4.P37 2009 
930.10285-dc22 2008040045 

ISBN 0-203-88146-X Master e-book ISBN 

ISBN10: 0-415-44877-8 (hbk) 
ISBN10: 0-415-44878-6 (pbk) 
ISBN10: 0-203-88146-X (ebk) 

ISBN13: 978-0-415-44877-2 (hbk) 
ISBN13: 978-0-415-44878-9 (pbk) 
ISBN13: 978-0-203-88146-0 (ebk) 



List of illustrations x 

List of tables xv 

Preface xvi 

Acknowledgements xviii 

1 Introduction 1 

2 A history of satellite remote sensing for archaeology 1 3 

Overview 1 3 
1900s-1930s 14 
1940s-1960s 18 
1970s 20 
1980s 23 
1990s 28 
2000 onwards 31 

3 Satellite image types 41 
Overview 41 

Google Earth™ 43 

NASA World Wind 51 

Corona High Resolution Space Photography/KH-7/KH-9 52 

KVR-1000 58 

Landsat 58 

SPOT 65 


SRTM 70 

High resolution imagery: Quickbird and IKONOS 72 



Other airborne sensors: RADARS AT, airborne thermal radiometry 78 

Data quality 79 


4 Processing techniques and imagery analysis 81 
Overview 81 

Imagery processing programs 84 

Integrating satellite images and aerial photographs and visual 

interpretation 85 

Georeferencing 88 

Band combinations 91 

Normalized Difference Vegetation Index 92 

Classification 94 

Thresholding 97 

Principal Components Analysis 97 

Land Use Land Cover Changes 99 

DEM techniques 100 

Hyperspectral studies 101 

Filtering 102 

RADAR/LIDAR analysis techniques 104 

GIS and remote sensing 106 

Combined techniques 109 

Towards automated archaeological site detection? 110 

5 Landscape approaches and project design 113 

Overview 113 

Landscape types 117 

Feature types 131 

How much can satellites detect? 144 

6 Case studies 147 
Overview 147 

The Peten, Guatemala 147 

Angkor Wat, Cambodia 151 

Xi'an, China 155 

Ubar, Oman 158 

Horns, Syria 160 

The Delta and Middle Egypt 165 

Conclusion 170 

7 Remote sensing and survey 173 
Overview 173 

Recording sheet overview 192 
Example recording sheet 201 
Conclusion 202 


8 Conservation, heritage management, and the ethics of remote 

sensing for archaeology 205 

Overview 205 

Conservation and heritage management 208 

Ethics 220 

9 Conclusion 233 

Bibliography 243 

Index 275 


Chapter 1 

1.1 Digital elevation model of Chaco Canyon, New Mexico, image courtesy 

of Google Earth™ Pro 2 

1.2 The Forum, Rome, image courtesy of Google Earth™ Pro 2 

1.3 Babylon, Iraq, image courtesy of Google Earth™ Pro 4 

1.4 Digital elevation model, Tell Tebilla, northeast Delta, image courtesy of 
Google Earth™ Pro 6 

1.5 Digital elevation model, Hadrian's Wall, northern England, image 

courtesy of Google Earth™ Pro 7 

1.6 Medinet Habu, Luxor, Egypt, image courtesy of Google Earth™ Pro 9 

1.7 Giza Pyramids, Egypt, image courtesy of Google Earth™ Pro 11 

Chapter 2 

2.1 Orthophoto image of Washington, DC (scale 1:583 m), imagery courtesy 

of NASA World Wind ^ ' 14 

2.2 Stonehenge, UK, image courtesy of Google Earth™ Pro 15 

2.3 Jerusalem (Dome of the Rock), Israel, image courtesy of 

Google Earth™ Pro 16 

2.4 Cahokia Mounds, Illinois, image courtesy of Google Earth™ Pro 17 

2.5 White Sands Proving Ground, image courtesy of Google Earth™ Pro 19 

2.6 Chaco Canyon, New Mexico, Orthophoto and Quickbird imagery, 

courtesy of Google Earth™ Pro and NASA World Wind 2 1 

2.7 Celone Valley, Italy, image courtesy of Google Earth™ Pro 22 

2.8 Ellis Island, New York City, New York, image courtesy of NASA 23 

2.9 Blue Ridge Mountains in Shenandoah National Park, image courtesy of 
Google Earth™ Pro 24 

2.10 Landsat image of Libya (scale 1:1248 km), image courtesy of NASA 

World Wind 25 

2.11 Land reclamation in Middle Egypt (scale 1:3413 m), note the areas being 
extended to the West for future development, 2008 image courtesy of 
Google Earth™ Pro 26 

2.12 Landsat image of Morton Fen, Linconshire, England (scale 1:609 rn), 

image courtesy of NASA World Wind 28 

2.13 Kharga Oasis, Egypt, image courtesy of Google Earth™ Pro 29 

2.14 Landsat image of Western Peloponnesus of Greece (scale 1:5300 m), 

image courtesy of NASA World Wind 30 


2.15 Photograph of the International Conference on Remote Sensing 
Archaeology, image by Sarah Parcak 33 

2.16 Sanmexia Reservoir, China, image courtesy of Google Earth™ Pro 34 

2.17 Boston, Massachusetts, orthophoto, image courtesy of 

NASA World Wind 35 

2.18 The Alamo, Texas, Orthophoto and Quickbird, image, courtesy of Google 
Earth™ Pro and NASA World Wind 37 

2.19 Southern California Landsat image, image courtesy of NASA 38 

Chapter 3 

3.1 The electromagnetic spectrum, adapted from http://www.ghcc.msfc.nasa. 
gov/archeology/images/remote_sensing/spectrum.gif, Image courtesy of 
NASA. " " 44 

3.2 Lake Peten Itza, Guatemala, 4-3-2 RGB Landsat, image courtesy 

of NASA 45 

3.3 Lake Peten Itza, Guatemala, 3-2-1 RGB Landsat, image courtesy 

of NASA 46 

3.4 Giza Pyramids, Egypt, image courtesy of Google Earth™ Pro 47 

3.5 Timothy Dwight College, Yale University, New Haven, CT, image 

courtesy of Google Earth™ Pro 48 

3.6 Aerial photograph of Trinity College, Cambridge University, UK, image 
courtesy of Google Earth™ Pro 49 

3.7 Cropmarks in the East Delta, Egypt, image courtesy of 

Google Earth™ Pro 50 

3.8 Tell el-Markha Fort, South Sinai, Egypt and the so-called southern 

"forts," images courtesy of Google Earth™ Pro 52 

3.9 Landsat images of Alabama in 1990 and 2000, images courtesy of NASA 
World Wind 55 

3.10 Photograph of Corona strip, image by Sarah Parcak 57 

3.11 Corona image of Tell Tebilla, Egypt, image courtesy of US Geological 
Service and Landsat image of Tell Tebilla, image courtesy of NASA 

World Wind 59 

3.12 Mt. Desert Island, Maine, Landsat image, image courtesy of NASA 

World Wind 61 

3.13 Sahara Desert, image courtesy of NASA World Wind 62 

3.14 Stonehenge, UK, image courtesy of NASA World Wind 63 

3.15 Mendes, Egypt, Quickbird image and Landsat image, images courtesy of 
Google Earth™ Pro and NASA World Wind ' 64 

3.16 East Delta, Egypt SPOT image, image courtesy of SPOT 66 

3.17 Lake Peten Itza, Guatemala, 3-2-1 ASTER image, image courtesy 

of NASA * 68 

3.18 Lake Peten Itza, ASTER, image courtesy of NASA 69 

3.19 SRTM image of Lima, Peru, image courtesy of NASA 7 1 

3.20 Quickbird images of Tell el-Amarna, image courtesy of Google Earth™ 

Pro 74 

3.21 SIR- A image of Egypt, image courtesy of NASA 76 


Chapter 4 

4.1 Mille Lacs Lake, National Wildlife Refuge, Minnesota, Landsat images, 
images courtesy of NASA 82 

4.2 Belize coast, Landsat images, images courtesy of NASA 86 

4.3 East Delta, Egypt, Corona image, image courtesy of US Geological 

Service 87 

4.4 North Sinai Lagoon system, Egypt, image courtesy of 

Google Earth™ Pro 88 

4.5 Georeferencing Dikirnis, East Delta Egypt, images courtesy of 

US Geological Service and Google Earth™ Pro 89 

4.6 Macchu Piccu, Peru, Landsat imagery, images courtesy of NASA 91 

4.7 Western China, Tongwangcheng, images courtesy of NASA 93 

4.8 Manuas, Brazil, X-SAR, image courtesy of NASA 94 

4.9 Lake Peten I tza, Guatemala, image courtesy of NASA 95 

4.10 The Great Wall of China, image courtesy of Google Earth™ Pro 97 

4.11 North Croatia, images courtesy of NASA 98 

4.12 Mansourah, Egypt, images courtesy of US Geological Service and Google 
Earth™ Pro 100 

4.13 DEM of Machu Picchu, Peru, image courtesy of Google Earth™ Pro 101 

4.14 Shell Mound, Cagayun River, image courtesy of Google Earth™ Pro 102 

4.15 Auburn University, Alabama, Quickbird, image courtesy of 

Google Earth™ Pro 103 

4.16 Mt. Ruapehu volcano, New Zealand, image courtesy of NASA 104 

4.17 New Orleans, LIDAR imagery, image courtesy of NASA 105 

4.18 Tannehill, Alabama, image courtesy of NASA 107 

Chapter 5 

5.1 The Acropolis, Athens, image courtesy of Google Earth™ Pro 115 

5.2 Rainforest in Brazil, image courtesy of Google Earth™ Pro 119 

5.3 Colosseum, Rome, image courtesy of Google Earth™ Pro 120 

5.4 Merv, Turkmenistan, image courtesy of Google Earth™ Pro 122 

5.5 Western Desert, Egypt Landsat, image courtesy of NASA World Wind 124 

5.6 Mt. Katahdin, Maine, image courtesy of Google Earth™ Pro 125 

5.7 Vanua Levu, Fiji, images courtesy of NASA and Google Earth™ Pro 127 

5.8 Tell Sariri, Delta, image courtesy of Google Earth™ Pro 129 

5.9 Siberia, image courtesy of Google Earth™ Pro 131 

5.10 Mohenjodaro, Pakistan, image courtesy of Google Earth™ Pro 133 

5.11 Arenal region of Costa Rica, image courtesy of Google Earth™ Pro 136 

5.12 Bamiyan Buddhas, Afghanistan, image courtesy of Google Earth™ Pro 137 

5.13 West Delta, Delta, Egypt, image courtesy of Google Earth™ Pro 140 

5.14 Arlington National Cemetery, image courtesy of Google Earth™ Pro 14 1 

5.15 Rapa Nui (Easter Island), Chile, image courtesy of Google Earth™ Pro 143 

5.16 Tuna el-Gebel, Egypt, image courtesy of Google Earth™ Pro 144 

5.17 Poverty Point, Louisiana, image courtesy of NASA World Wind 145 

5.18 Photograph of coring in Middle Egypt, image by Sarah Parcak 145 


Chapter 6 

6.1 Tikal, Guatemala, image courtesy of NASA 148 

6.2 The Peten, Guatemala, Landsat, image courtesy of NASA 149 

6.3 Tikal, Guatemala, image courtesy of Google Earth™ Pro 150 

6.4 The Peten, Guatemala, Landsat, image courtesy of NASA World Wind 151 

6.5 Angkor Wat, Cambodia, Landsat, image courtesy of NASA World Wind 152 

6.6 Angkor Wat, Cambodia, Landsat, image courtesy of NASA World Wind 153 

6.7 Angkor Wat, Cambodia, image courtesy of Google Earth™ Pro 154 

6.8 Xi'an, China, image courtesy of NASA World Wind 155 

6.9 Xi'an, China, image courtesy of Google Earth™ Pro 157 

6.10 Xi'an, China, Landsat, image courtesy of NASA World Wind 158 

6.11 Ubar, Oman, image courtesy of Google Earth™ Pro 159 

6.12 Ubar, Oman, image courtesy of NASA World Wind 159 

6.13 Horns, Syria, Landsat, image courtesy of NASA World Wind 161 

6.14 Horns, Syria, Landsat, image courtesy of NASA World Wind 163 

6.15 Horns, Syria, image courtesy of Google Earth™ Pro 163 

6.16 Middle Egypt, photograph by Sarah Parcak 167 

6.17 Middle Egypt, image courtesy of NASA 168 

6.18 Ashmunein, Middle Egypt, photograph by Sarah Parcak 169 

6.19 Ashmunein, Middle Egypt, images courtesy of NASA and Google 169 

6.20 Middle Egypt Survey 2005, photograph courtesy of Sarah Parcak 171 

6.21 Tell Sheikh Masara, images courtesy of Google Earth™ Pro and Sarah 

Parcak 171 

Chapter 7 

7.1 Delta survey, Egypt, photograph by Sarah Parcak 174 

7.2 Middle Egypt, image courtesy of Google Earth™ Pro 175 

7.3 Sarah Parcak using a differential GPS, photograph by Sarah Parcak 177 

7.4 Middle Egypt Survey Project team, photograph by Sarah Parcak 178 

7.5 Fields in Middle Egypt, photograph by Sarah Parcak 178 

7.6 Urban surveying, Egypt, photograph by Sarah Parcak 179 

7.7 Sherd scatters in a field, photograph by Sarah Parcak 181 

7.8 Middle Egypt Survey Project 2004, photograph by Sarah Parcak 182 

7.9 Tell ed-Da'ba, East Delta, image courtesy of Google Earth™ Pro 183 

7.10 Farmer, Middle Egypt, photograph by Sarah Parcak 185 

7.11 Pottery, Middle Egypt, photograph by Sarah Parcak 185 

7.12 East Delta survey, photograph by Sarah Parcak 188 

7.13 Tell Sharufa, East Delta 2003 Delta survey, photograph by Sarah Parcak 189 

7.14 East Delta Survey, photograph by Sarah Parcak 191 

7.15 Middle Egypt Survey workbook, image by Sarah Parcak 193 

7.16 Modern Delta maps, photograph by Sarah Parcak 196 

7.17 Tanis, Egypt, image courtesy of Google Earth™ Pro 198 

7.18 Kharga Oasis, Egypt, image courtesy of Google Earth™ Pro 199 

7.19 Kom el-Ahmar, Middle Egypt, images courtesy of Google Earth™ Pro 

and Sarah Parcak 201 


Chapter 8 

8.1 Taj Mahal, India, image courtesy of Google Earth™ Pro 206 

8.2 Karnak Temple, Egypt, image courtesy of Google Earth™ Pro 207 

8.3 Luxor, Egypt, image courtesy of Google Earth™ Pro 209 

8.4 Southeast India in Tamil Nadu along the Mahabalipuram, image courtesy 

of NASA World Wind 210 

8.5 Takwa, Kenya, image courtesy of Google Earth™ Pro 212 

8.6 Mont St. Michel, France, images courtesy of NASA World Wind 214 

8.7 Ismu al-Arus, Middle Egypt, photograph by Sarah Parcak and 2008 
Quickbird image courtesy of Google Earth™ 216 

8.8 Jokha, Iraq, image courtesy of Google Earth™ Pro 217 

8.9 Merv, Turkmenistan, image courtesy of Google Earth™ Pro 219 

8.10 Supreme Council for Egyptian Antiquities, photograph by Sarah Parcak 220 

8.11 Tell el- Amarna, Egypt, image courtesy of Google Earth™ Pro 225 

8.12 Mt. Ararat, Turkey, image courtesy of Google Earth™ Pro 227 

8.13 Great Zimbabwe, Zimbabwe, image courtesy of Google Earth™ Pro 231 

Chapter 9 

9.1 Olduvai Gorge, Tanzania, image courtesy of Google Earth™ Pro 235 

9.2 Kill Site, Montana, image courtesy of Google Earth™ Pro 235 
9-3 Moundville, Alabama, image courtesy of Google Earth™ Pro 237 

9.4 Pompeii, Italy, image courtesy of Google Earth™ Pro 238 

9.5 Umma, Iraq, image courtesy of Google Earth™ Pro 240 


Chapter 3 

3 . 1 Comparative satellite imagery and multiple imagery viewing 

programs table 42 

3.2 Corona and KH-9 imagery, adapted from 
satellite/declassl.html 54 

3.3 Chart of Landsat band values, adapted from 
landsat/17.html 60 

3.4 SPOT-4 values, adapted from 
172-spot-images.php 65 

3.5 ASTER spectral values, adapted from 
ast_llb.asp 67 

Chapter 4 
4.1 Techniques for imagery analysis per main imagery type used in 

archaeology 82 

Chapter 5 

5 . 1 Landscape types and the most applicable satellite images for 
archaeological projects 114 

5.2 Archaeological site types and the most applicable satellite images for 
archaeological projects 114 

5.3 Landscape types and the most applicable analytical techniques for 
archaeological work 118 

5 .4 Archaeological site types and the most applicable analytical techniques 

for archaeological work 118 

Chapter 7 
7.1 Survey times based on relative town sites during 2003/4 Egypt surveys 203 


It is my sincere hope that students, professional archaeologists, archaeological researchers 
and all people fascinated by the potential for satellite remote sensing in archaeology will 
find this book useful. It is, more than anything else, a product of my thinking back to 
my graduate student days, and what I wish had existed then to help me get started with 
my work. 

Before I proceed, it is important for me to explain why I have chosen to write this 
book in the first place. The use of satellite imagery in archaeology is a topic about 
which I am passionate, having spent eight years developing methods for archaeological 
site location in different regions of Egypt (Sinai, the Delta, and Nile Valley), teaching 
remote sensing courses, and (two years ago) starting a remote sensing laboratory. When 
I started doing research for a then undergraduate research paper in an introductory 
remote sensing course, I was struck by how little information could be found for general 
satellite remote sensing applications in archaeology. This became more apparent during 
my PhD years. Few papers existed that described how remote sensing could be applied 
to Egyptian archaeology, and no papers existed for the use of satellite remote sensing in 
the Nile Valley floodplain or Delta. I, quite literally, had to make it up as I went along, 
with all the expected pitfalls (significant trial and numerous errors) of doing something 
that had never been done before. Fortunately, my colleagues had written a number of 
excellent papers on satellite archaeology in other regions of the world, and I could rely 
on them for specific discussion points. Fieldwork proved to be something else entirely. 
Large-scale survey to locate previously unknown archaeological sites is not a significant 
part of Egyptian archaeology (Parcak 2008), and I had to develop a method for recording 
sites found during my remote sensing analysis. 

As a result of that work and subsequent surveys, I had thought a great deal about 
initially editing a general methods book for satellite remote sensing for archaeology. 
Discussing the subject with my colleagues, many of whom I had approached about 
contributing to such a volume, I was encouraged to write the book, period. I stated that 
I would be more inclined to write the book at a future date, when I had many more surveys 
beneath my belt. After several lengthy discussions with the same colleagues, and many 
PhD students, I realized just how much of a gap existed between an ever-growing interest 
in the field of satellite archaeology and the materials available to those who wished to 
pursue it. Becoming the director of a remote sensing lab and teaching remote sensing 
courses (with many anthropology students) proved to be the impetus for moving the 
project ahead. Seeing first hand the remote sensing learning process allowed additional 
insights into how to present a methods book. Students and colleagues responded well 


to a more practical and hands-on approach, which is the general approach I have taken 
in this volume. 

It should be noted that 99 percent of the satellite images in this book represent original 
remote sensing work and analysis by the author. Many imagery analysis techniques were 
based on archaeological studies from articles and books, but the images in this book 
are not specifically from those studies. This was done to provide specific examples of 
archaeological remote sensing subjects discussed in the book. It should also be noted 
that many of the images do not have scale bars, but instead have the scales indicated in 
the captions. This was done on purpose: although some of the images could have had 
scale pieces inserted, many of the images exist to illustrate a specific remote sensing 
point that requires all of the image to be clear. Given the book image sizing, inserting 
scale pieces would have detracted from the function of the images. This follows the 
example of the remote sensing textbook Remote Sensing and Image Interpretation 
(Lillesand et al. 2004). For additional information regarding obtaining satellite imagery 
and learning more about remote sensing, please visit this book's website, located at 
www. rout ledge . com 


One cannot produce a book, especially one on such a focused topic, without the help 
of countless individuals. My initial thanks must go to Larry Bonneau of the Center for 
Earth Observation (CEO) at Yale University, whose patience with a Yale undergraduate 
helped her remote sensing career to start, as well as Ron Smith, director of the CEO, 
for his general support. When doing my initial remote sensing research at Cambridge 
University, I was lucky to have the help, support, and friendship of Janine Bourriau, 
Sally- Ann Ashton, Lucilla Burn, Ben Roberts, Chris Thornton, Hilary Soderland, Vicky 
Donnelly, Andrew Bednarski, Jenna Spellane, Fred and Jenny Hagen, Dirk Notz, Anne 
Chen, Shakira Christodoulou and family, Catherine French, Flora Spiegel, Ruth Adams, 
and James Williams, all of whom contributed to my academic as well as personal life. 

There are far too many individuals who I have encountered at remote sensing, archae- 
ology, and Egyptology conferences, and in the field to thank individually. I would like 
to list a few people whose support and encouragement at conferences and elsewhere 
have made the work I do far more enjoyable: Mirek Barta, Olaf Bubenzer, David Gill, 
Mauritzio Forte, Gao Huadong, Salima Ikram, Ellen Morris, Thomas Schneider, Janice 
Stargardt, and Kasia Szpakowska. For their amazing hospitality and support of my work 
when attending the International Conference on Remote Sensing Archaeology, I want 
to thank the Chinese Institute for Remote Sensing Archaeology, in particular Changlin 

My escapes to Egypt for fieldwork have been made all the more pleasant by a wonderful 
group of people I am lucky enough to call my dig family: Zoe McQuinn, Kei Yamamoto, 
Debby Donnelly, Patrick Carstens, Rexine Hummel, Fran Cahill, and Larry Pavlish, 
whose untimely death in 2007 has left a big gap in my life. I can only imagine what 
his comments would be on this book. In Egypt my work is facilitated by the Supreme 
Council for Egyptian Antiquities, the Egyptian Antiquities Information Service, and 
the American Research Center in Egypt (with special thanks to Madame Amira Khattab 
and Mr Amir Hamid). 

My academic influences are many, but I can certainly attribute my budding interest 
in Egyptology to William Kelly Simpson, under whom it was an honor to work and 
study at Yale. Colin Shell, my PhD advisor at Cambridge, challenged me to "think big" 
with remote sensing. My PhD supervisor, Barry Kemp, has been my academic mentor, 
and I will never be able to thank him properly for all his help, support and guidance. 
My remote sensing interest has been encouraged and supported by Ron Blom and Ray 
Williamson, whose encouragement via email and in person has been most welcomed. 
For their amazing support and in loco parentis in far-flung regions of the world, I owe a 


major debt to Pay son and Fran Sheets. Pay son has been a professional mentor to me, and 
I cannot express in words my gratitude for all he has done and continues to do. 

Here at the University of Alabama at Birmingham, many people have supported my 
work, and the Laboratory for Global Health Observation (LGHO), which I started in 
2007. Tennant Mc Williams has been a constant supporter and mentor, and without him 
the LGHO would never have started. Max Michael, Jean Ann Linney, and members of 
the LGHO advisory board keep pushing me ahead in the broader remote sensing world. 
Dick Marchase saw (and continues to see) the big picture. I owe funding support to many 
sources, including UAB's NSF ADVANCE program (with special thanks to Claire Peel 
and Sherry Scott Pigford), the UAB Faculty Development Grant, the Department of 
Anthropology (with many thanks to Jannie Williamson and Kevin Johnson), and the 
School of Social and Behavioral Sciences. My friends and colleagues at UAB and in 
Birmingham, are too numerous the mention, but a few deserve special thanks for their 
support: Jim and Liz Reed, Dave and Yikun Schwebel and family, Sara Helms, Erik 
Angner and Elizabeth, Gypsy Abbott and George, Bob Novak, Michele Forman and 
Erik Lizee, and Rosie O'Beirne and family. Ami and Kenyon Ross and their family are 
wonderful, special people, and make being here a joy. 

LGHO students have made me a better teacher and better researcher. Donna Burnett, 
LGHO Program Manager, has helped immeasurably. NASA's support — financial and 
otherwise — has been amazing. A major academic debt is due to NASA researcher, 
and now Professor, Tom Sever, who promoted, and in many ways started, the field of 
satellite remote sensing for archaeology (and, in a sense, this book celebrates the 25th 
anniversary of the field). For their help in editing chapters and other aspects, thanks 
are due to Donna Burnett, Scott Silver, Lauren Ristvet, Greg Mumford, Payson Sheets, 
Errin Weller, David Gill, Chris Thornton, Ben Roberts, Dave Cowley, Adam Pinson 
(bibliography), and David Gathings (images). Naturally, all errors are my own. 

Routledge has been wonderful to work with on this project. They have always 
responded to my queries quickly, and have made the process of book writing much 
easier. I owe special thanks to Matthew Gibbons for his initial encouragement of this 
project, and to Lalle Pursglove for her help and support. 

For their support and love, I need to thank my wonderful family: Mom, Dad, Aaron, 
Gram, Sue, Emily, Steve, Mike, Phil, Cindy, Harva, Kate, Barbara, Gordon, Ben, David, 
Jeanette, and Grammy. Thank you for always being there. Finally, this book is dedicated 
to the two people who have made the biggest difference in my life: My grandfather (now 
deceased), Harold Young, former world-renowned Forestry Professor at the University 
of Maine, and one of the pioneers of Aerial Photography in Forestry, who was a model 
human being and academic; and to my husband, Greg Mumford, who is amazing every 
day, and who has taught me more things than I could write in 100 books. 


Our Earth is ever changing. Continental plates shift, ocean levels rise and fall, mountains 
and shores erode, deserts and glaciers move, and natural disasters change the face of the 
globe. These are natural processes that have been shaping the surface of the Earth for 
billions of years. Over a much shorter period of time, formerly insignificant organisms 
known as homo sapiens began to shape natural environments to fit their needs. This 
is not unusual for organisms: Certainly, all plants and animals will transform natu- 
ral surroundings to fit their needs. However, humans were different in that they not 
only sought actively to control their surroundings, but they managed to colonize every 
habitable surface ecosystem on the planet, from high mountains to low valleys, and 
from Arctic tundra to equatorial deserts. As the population of humans grew and their 
social structures became more complex, the effect of these creatures on the surface of 
the Earth became more noticeable. Indeed, as both social and cognizant creatures with 
a penchant for controlling the world around them, human beings developed a unique 
relationship with their surroundings. Understanding this special relationship between 
humans and their environment, which archaeologists, geographers, and other scholars 
often call "social landscapes," is one of the most pressing issues facing us in the 21st 

How scholars and scientists detect these social landscapes has evolved with advances in 
technology, and stands to have an enormous impact on the fields of archaeology, history, 
geography, environmental sciences, and other related fields. How past places on Earth 
can be made visible (Gould 1987) is a topic that has received significant attention in 
the media and in classrooms. Although most archaeology relies on visual recognition of 
past remains, seen in Figure 1.1 (e.g. "the deposit of statues was found in a foundation 
trench," or "level 3B in square IV contains mainly broken faience amulets from the 
Middle Kingdom"), the majority of archaeological remains and features are hidden 
from view, whether buried underground by environmental processes (e.g. ancient river 
courses, old lakes, field boundaries), covered over by modern towns (e.g. Rome, seen in 
Figure 1.2, or in Greece), or by modern vegetation (e.g. forests covering settlements in 
Europe). The use of satellite remote sensing can not only reveal these hidden, or so-called 
"invisible," remains, but can place them in much larger contexts, showing past social 
landscapes in all their complexity. 

What is satellite remote sensing, and how can it be applied to archaeological research? 
Satellite remote sensing is the specific application of satellite imagery (or images from 
space) to archaeological survey (Zubrow 2007), which entails searching for ancient sites 


1.1 Digital elevation model of Chaco Canyon, New Mexico (scale 1:122 m), 2008 image 
courtesy of Google Earth™ Pro. 

Figure 1.2 The Forum, Rome (scale 1:244 m), 2008 image courtesy of Google Earth™ Pro. 


on a particular landscape at different scales (Wilkinson 2003). While aerial photography 
(Deuel 1973; Kenyon 1991; Wilson 2000), geophysics (Schmidt 2001; Witten 2005), 
laser scanning of monuments including Stonehenge, see Goskara et al. 2003), virtual real- 
ity (Broucke 1999; Barcelo et al. 2000), imagery analysis within geographic information 
systems (GIS) (Chapman 2006; Conolly and Lake 2006), and satellite imagery analysis are 
all forms of remote sensing — and all are invaluable for archaeological investigations — 
the application of satellite remote sensing in archaeology is the primary focus of this 

All forms of remote sensing, including imagery analysis in a GIS, geophysics, satellite 
remote sensing, and aerial photography are concerned with the identification of "anthro- 
pogenic" features in such a landscape, whether they are detecting a structure, such as a 
building or a town, or a system of irrigation channels and roads. In fact, "remote sensing" 
is a term that refers to the remote viewing of our surrounding world, including all forms 
of photography, video and other forms of visualization. Satellite remote sensing has the 
added advantage of being able to see an entire landscape at different resolutions and 
scales on varying satellites imagery datasets, as well being able to record data beyond 
the visible part of electromagnetic spectrum. Remote sensors can analyze imagery so that 
distracting natural invisible or anthropogenic features on that landscape (such as forests 
or buildings) can be made, while ancient remains previously invisible to the naked eye 
appear with great clarity. In this way, satellite remote sensing can reconstruct how past 
landscapes may have looked, and thereby allows a better understanding of past human 
occupation of those landscapes. 

How can people apply satellite remote sensing from space to archaeological investi- 
gations? As human beings, we can only see in the visible part of the electromagnetic 
spectrum. The electromagnetic spectrum extends far beyond the visible parts of the 
spectrum to the infrared, thermal, and microwave, all of which have been used by archae- 
ologists to see through or beneath rainforests, deserts, and modern debris to locate past 
remains. Recent archaeological findings using the electromagnetic spectrum include the 
discovery of many ancient Mayan sites in Guatemala (Saturno et al. 2007), ancient water 
management strategies at Angkor Wat (Evans et al. 2007), and details of how Easter 
Island statues were transported (Hunt and Lipo 2005), to name a few. Satellites record 
reflected radiation from the surface of the Earth in different parts of the electromagnetic 
spectrum, with every satellite image type recording this information slightly differ- 
ently. A more detailed discussion of the electromagnetic spectrum appears in Chapter 3. 
Ancient archaeological remains will affect their surrounding landscapes in different 
ways, whether through altering the surrounding soils, affecting vegetation, or absorbing 
moisture at a greater or lesser rate. On the ground, we cannot see these subtle landscape 
changes visually. Archaeologists are not necessarily "seeing" beneath the surface with 
multispectral satellite imagery when using remote sensing techniques; they are actually 
seeing the discrepancy between higher and lower moisture and heat contents of buried 
walls, which affect the overlying soils, sands, and vegetation. How individuals choose to 
manipulate satellite data to obtain these results will vary greatly depending on landscape 
type, satellite image type, and the overall research goals of the archaeological project. 

The majority of satellite survey work in archaeology, however, has focused on visible 
archaeological site detection; enlarging satellite images to find sites or landscape features. 
There is much we can miss by being on the ground by not seeing things from an 
aerial perspective, as shown by Figure 1.3. Visual satellite datasets are a valuable tool 


Figure 1.3 Babylon, Iraq (scale 1:1000 m), note modern reconstruction in the northern part of 
the site, 2008 image courtesy of Google Earth™ Pro. 

for overall landscape visualization and site detection, as satellites give a far broader 
perspective on past landscapes. However, visual data represents only a tiny proportion of 
what satellites can offer the archaeologist in feature detection. Not making use of the full 
electromagnetic spectrum leaves countless archaeological features or sites undiscovered. 

The development of this methodology has already contributed to the discovery of 
tens of thousands of new archaeological sites and features across the globe, with many 
of these studies discussed in detail here. If these case studies can serve as an example, 
there must be many hundreds of thousands (if not millions) of additional archaeological 
sites and features that remain currently undiscovered. This is particularly true, since in 
satellite remote sensing a specific methodology has not yet been developed to detect 
obscured ancient sites and features using the full range of satellite imagery analysis 
techniques. The ramifications of such new discoveries would be massive for historical 
and environmental research programs, and give tremendous insights into past social 

The second level of archaeological inference in satellite remote sensing is survey, or 
"ground truthing." Although finding potential archaeological sites or features on a com- 
puter screen is important, and relies on detailed scientific analysis, it is impossible to 
know more about an archaeological site without ground confirmation. Using a global 
positioning system (GPS), researchers can pinpoint areas on a computer screen for sur- 
face archaeological investigation (Arnold 1993). With the ever-increasing emphasis on 
reducing costs, satellite remote sensing allows archaeological teams to find sites and fea- 
tures that they would otherwise have had to locate randomly. This has certainly happened 


before in archaeology: Many tombs and deposits have been found accidentally, such as 
the catacombs of Alexandria, Egypt, which were found when the top of the tomb shaft 
was breeched by donkeys walking across it (Empereur 2000). This is hardly an ideal 
method for locating new sites. 

In some cases, surface material culture may indicate the presence of an archaeological 
site, while in other cases the site will be revealed by growth patterns in vegetation, chem- 
ical alterations in the soil, or close proximity to a covered natural feature. On the ground 
during general archaeological survey, we can certainly observe differently colored soils 
(Van Andel 1998) and different aspects of material culture. Surveying an entire landscape 
through visual observation of these features and the collection of material culture allows 
archaeological teams to make observations about land occupation over time. Satellite 
remote sensing, in combination with ground survey, coring, and excavation, allows for a 
holistic landscape reconstruction, by enabling the detection and assessment of invisible 
sites and features. Both visible and invisible archaeological features combine to give a 
better understanding of anthropogenic effects on past landscapes. 

The third level of inference in satellite remote sensing is targeted excavations, in 
which archaeologists open a limited number of trenches that are strategically placed 
to give the maximum amount of information about that site or that landscape. In 
a normal excavation in which satellite remote sensing is not involved, archaeologists 
often will use geophysics or ground survey to determine the best placement for their 
trenches. In instances where geophysics is not used, perhaps the archaeological team 
will decide to place their trenches at higher locations (where preservation is better), or 
where specific surface material culture indicates certain types of subsurface structures 
(e.g. large amounts of slag appearing on the surface of an archaeological site may indicate 
an industrial area beneath the ground — Figure 1.4). 

When using satellite remote sensing for excavation, however, the archaeological team 
can use the electromagnetic spectrum and broader visual detection to reveal features not 
apparent on the ground. Satellite remote sensing, in a sense, acts as an aerial geophysical 
sensor, identifying potential buried features such as walls, streets, or houses. Once the 
archaeological team knows the exact location of any potential feature, they can target 
their excavation trenches accordingly. Satellites may be more cost-effective than other 
geophysical methods, as they can be used to analyze broader stretches of land, but 
everything will depend on the project aims of the archaeological team. The differences 
described here highlight how the research agenda of a traditional archaeologist differs 
from that of one informed by satellite remote sensing techniques. 

While there is great interest in the use of satellite remote sensing, people are generally 
not aware of the full potential of satellite data for archaeological work. Satellite remote 
sensing studies are certainly becoming more popular, evidenced by increasing numbers 
of related articles in journals such as the Journal of Field Archaeology, Antiquity, and 
Archaeological Prospection, and an increasing presence of satellite remote sensing in popular 
media. Many general remote sensing handbooks exist (Jensen 1996; Lillesand et al. 
2004), and form required readings in introductory remote sensing courses. These books, 
while excellent reference tools, can be too technical for people with a more general 
interest in satellite remote sensing. This book does not claim to be a general remote 
sensing introduction or even an environmental remote sensing book (Jensen 2000): It is 
a book aimed at describing and evaluating the numerous applications of satellite remote 
sensing to global archaeological landscape evaluation, and is written to be accessible 


to archaeologists, students, and others interested in applying this technology to their 
work or learning more about the subject of satellite archaeology. Hands-on coursework is 
required to learn specific remote sensing programs, much like any scientific specialization 
in archaeology, but few courses exist at present dedicated to the teaching of satellite 
remote sensing in archaeology. 

The choice to use remote sensing as one's research is another topic. Does the existence of 
the technology alone merit developing an interest in the subject? Satellite remote sensing 
visualizes the confluence of human history and the environment, which archaeologists 
spend much of their time reconstructing. Anyone with an interest in comparing regional 
and national influences on a local level can benefit from having a better understanding 
of landscape changes and how they may have influenced site or feature placement. The 
very nature of archaeology is concerned with exposing hidden things, largely buried 
beneath the ground, to answer larger historical and anthropological questions. Satellite 
remote sensing in archaeology is virtually identical then to the larger scope archaeology, 
although the features it reveals are not necessarily buried in the same way. As in all 
scientific methods of analysis in archaeology, there is a right and a wrong way to conduct 
satellite remote sensing studies and related ground surveys. 

This book will start with a detailed history of satellite remote sensing in archaeology 
(Chapter 2), which will emphasize the general development of satellite remote sensing 
versus ongoing developments in archaeology and remote sensing. Gaining an apprecia- 
tion of how the field developed is important, because through understanding the history 
of satellite remote sensing, one can better appreciate where the field is headed. 

Figure 1.4 Digital elevation model, Tell Tebilla, northeast Delta (scale 1:78 m). Note the 
checkerboard pattern from surface scraping during the 2003 excavation season, 2008 
image courtesy of Google Earth™ Pro. 


Archaeologists and other specialists have already written books on aerial photography 
(Deuel 1973; St. Joseph 1977; Riley 1982; Bourgeois and Marc 2003; Brophy and 
Cowley 2005), but it is necessary to provide an overview of basic concepts of remote 
sensing as they started in the early 1900s with the advent of aerial archaeology. It will 
also be important for archaeologists to understand the advantages and disadvantages of 
using satellite remote sensing in relationship to aerial photography. Used together, they 
present powerful past landscape visualization tools. 

Chapter 3 discusses the various types of satellite imagery available to archaeologists, 
helping with the selection of most appropriate imagery, and provides information about 
ordering each image type. Access to satellite imagery is influencing how archaeologists 
conduct their fieldwork, while giving more individuals the ability to understand past 
landscapes. This access will only continue as free viewing programs, such as Google 
Earth™ or WorldWind, improve their resolution (Figure 1.5). Obtaining free satel- 
lite imagery is an important issue because adequate money for recent imagery may 
not be available. Chapter 3 also discusses the advantages and disadvantages of each 
satellite image type along with some of the various archaeological projects that have 
applied them. 

Chapter 4 provides a developing archaeological remote sensing methodology, from 
basic remote sensing techniques to complex algorithms, which will allow cross appli- 
cations of techniques across diverse regions of the globe. This chapter provides the 
most technical detail in the book. Satellite images are especially useful for mapmak- 
ing due to the paucity of high quality or recent maps in different regions and nations. 

Figure 1.5 Digital elevation model, Hadrian's Wall, northern England (scale 1:50 m), 2008 
image courtesy of Google Earth™ Pro. 


How trustworthy are such maps for archaeological analysis? As Irwin Scollar (2002) 
points out, maps are not images, and landscapes tend to be altered according to the 
preferences of the maker, thus making the whole field of satellite map generation sub- 
jective. This question extends beyond mapping, however, as a wide range of techniques 
are required to model past sites and features. Good science promotes non-destructive 
archaeology, while appropriate training augments the optimum application of satellite 
remote sensing in archaeology. Describing how satellite remote sensing is different from 
geophysics and GIS is also important, as the three subjects often appear together in 
case study books and conference proceedings (Scollar et al. 1990; Zubrow et al. 1990; 
Behrens and Sever 1991; Bewley and Raczkowski 2002; Wang and Huadong 2004; 
Johnson 2006; Hamilton 2007; Peebles 2007; Wiseman and El-Baz 2007). 

Satellite remote sensing in archaeology archaeological remote sensing is still a devel- 
oping field, and much testing is required to advance it. Distinguishing an archaeological 
site from a similar, but non-related signature in satellite imagery requires knowledge 
of the advantages and disadvantages of using different remote sensing techniques. Even 
archaeologists who have accumulated many years of experience in satellite remote sensing 
can make incorrect assumptions about potential features revealed by satellite imagery. 
One never stops learning with remote sensing: Training, updating skills, and contin- 
uous and varying field experience enables those using remote sensing to stay on top of 
this dynamic discipline. At present, a broad range of satellite archaeology articles are 
accepted for publication, some of which are more archaeological, others of which are 
more remote sensing focused. However, this merely reflects that diverse specialists exist 
who bridge the gap between these two evolving fields. Chapter 4 covers some techniques 
for interpreting landscapes to assist with overall project work, and strategies for ground 
surveys, attempting to span both archaeological and remote sensing approaches. 

Chapter 5 considers how and why archaeologists make particular choices regarding 
techniques and satellite images in their excavation and survey work, especially regarding 
major landscape site types. How individuals comprehend the development of ancient 
to modern landscapes is not a straightforward topic. Determining the geographical 
extent and time-span of a project is the first stage that should be addressed prior to 
archaeological analysis. Understanding how satellite remote sensing can contribute to 
a better understanding of macro- and micro-landscape studies is also crucial, especially 
when deciding on the types of imagery and the techniques of analysis required. At the 
macro-settlement scale, more broad-scale landscape issues are apparent, including overall 
environmental, economic, political, and social factors. Remote sensing assists with exam- 
ining archaeological site placement, and has detected past river courses (James 1995), 
water sources (McHugh et al. 1988) or geological factors (Ostir and Nuninger 2006) 
that might have influenced site placement or abandonment. At the micro-settlement 
scale, such factors are apparent at a local level and are assessed via place-specific docu- 
mentation or material culture remains. Satellite imagery may aid in determining where 
to excavate, or may render excavation unnecessary through specific feature detection. 
Satellite remote sensing may also encourage archaeologists to integrate either top-down 
and/or bottom-up models (Campana and Forte 2006). Remote sensing is, by necessity, a 
"top-down," approach, while the search for archaeological data to support "bottom-up" 
models takes into account broader factors. 

Prior to discussing the implications of satellite remote sensing in archaeology, 
Chapter 5 reviews what satellite imagery can detect, covering a full range of 



Figure 1.6 Medinet Habu, Luxor, Egypt (scale 1:159 m), extensive unexcavated archaeological 
remains surround the temple, 2008 image courtesy of Google Earth™ Pro. 

archaeological site types that one might encounter during satellite remote sensing anal- 
ysis. One key question is how can an archaeological site, or past landscape, be defined? 
There is no one correct definition of what constitutes an archaeological site, other than 
descriptions defining areas of varying sizes containing past remains. Geographical areas 
containing temples and tombs come to mind immediately but these are only a minis- 
cule sample of the sites surviving from antiquity (Figure 1.6). Would a single sherd 
in a modern agricultural field be considered a true site (Bintliff and Snodgrass 1988)? 
Theoretically, one could associate material culture debris with a specific place, includ- 
ing a number of sherds scattered over a raised area of earth, which might reflect an 
ancient rest stop, campsite, or habitation area. A single sherd in a field can be indica- 
tive of numerous processes: Environmental agencies (e.g. flooding) might have moved 
the sherd from its original location, perhaps several kilometers away; the sherd might 
indicate an underlying site beneath, or might have been discarded by past or present 
peoples in transit. We cannot know for certain what it represents without further context 
and exploration. For instance, does it date to a period and transit route between known 
sites? Hence, any past remains should constitute a significant piece of archaeological 
data, whether it be a single sherd, a former village beneath a modern town, or a temple 

The previously mentioned topics lead to some further discussion concerning how 
archaeologists can evaluate remotely explored landscapes. Chapter 6 provides addi- 
tional strategies for analyzing features from space, detailing six case studies covering 
remote sensing approaches to ancient landscapes ranging from rainforests to floodplains, 


and from deserts to grasslands. The chapter discusses Peten (Guatemala), Angkor Wat 
(Cambodia), Xi'an (China), Ubar (Oman), Horns (Syria), and the northeast Delta and 
region around Tell-el-Amarna (Egypt). The technical approaches and satellite imagery 
types chosen by each archaeological team are examined. The successes and failures of 
each approach are described, of particular note for other archaeologists who may wish 
to work in similar or related regions. 

At present, various archaeologists are contributing to the development of satellite 
remote sensing in archaeology through much needed studies in methodology. Assess- 
ing ongoing and past landscape changes, however, should be one of the main goals for 
satellite archaeology, and how each project has approached this will be discussed. Topo- 
graphical changes over time have influenced how people interacted with their local and 
regional resources, and how and why populations settled in various places initially. These 
changes are often partially or sometimes entirely obscured today, but it remains impor- 
tant to analyze how ancient sites developed in conjunction with their local landscapes. 
Landscapes are usually dynamic entities, changing over time according to environmental 
patterns at a local, regional, and global scales in tandem with global climate events as 
well as changes in the social landscape. Landscapes are not static and singular entities: 
They are palimpsests, with satellite imagery analysis helping to trace varying levels of 
change over time and space. How individual projects view these assorted layered land- 
scapes via satellite data, survey work, and in some cases, excavation, will be evaluated, 
with further guidance suggested for similar future projects. 

Chapter 7 introduces in-field considerations and ground survey. Ground survey is a 
crucial component in satellite remote sensing for archaeology. Only ground-truthing 
can currently determine data for newly discovered sites, while visualizing landscapes 
with layers of supplementary survey data can aid in reconstructing past landscapes. 
Beyond the scope of assessing past landscapes, archaeologists may face many fieldwork 
challenges. Locating archaeological sites using satellite remote sensing may signify little 
if there is not sufficient funding to verify preliminary results through ground survey. 
Understanding past human— environment interactions with satellite remote sensing is 
also much trickier without a good understanding of present day human— environment 
interactions, which aids in fieldwork, site preservation, and assessing ancient landscapes. 
Numerous satellite archaeology studies have focused on archaeological site location via 
computer analysis, but far fewer studies describe fully the techniques employed to locate 
sites on the ground, or how the discovered sites correlate with the original analysis and 
overall historical implications. Specialists using satellite remote sensing in archaeology 
need to move from an air-based viewpoint to a ground-based one in order to reveal 
broader, more meaningful archaeological landscapes. 

Defining a "site" in the broader context of its archaeology and its social landscape is 
not a straightforward process during ground survey. Archaeologists can only begin to 
appreciate sites fully if their historical and geographic contexts are examined. Describing 
the physical nature of an area of archaeological interest is only the first step: Once 
archaeologists have examined the material culture remains, the site can be placed in its 
local, regional, continental, and even global contexts. Thus, individual site description 
is a process of evolution rather than a finite academic exercise. Initial observations play 
a crucial role in how future studies are conducted. Both past and present formation 
processes are equally important in how ancient sites obtain their present condition, and 
comprehending the nature of these processes assists in making preliminary assessments 



and asking the right questions in the field. Thus, it will be important to outline various 
approaches to recording these formation processes. 

Other issues (Chapter 8) entail ethical considerations for the use of satellite imagery, 
and how remote sensing can be applied to efforts in conservation and heritage man- 
agement. In some situations, a satellite study area may be inaccessible due to war, 
or other serious internal problems (e.g. natural disasters). Urban development, related 
population growth, and environmental changes (Akasheh 2002) can all be detected via 
remote sensing analysis, and stress the importance of locating archaeological sites to 
preserve and protect them. Unless broader collaborative efforts are encouraged between 
archaeologists and the host government agencies responsible for protecting sites, little 
can be done to preserve past remains. Archaeological landscapes are threatened every- 
where (Figure 1.7), including ancient sites that are not visible from space. For example, 
deep-sea, drag-net trawling has destroyed many ancient and modern shipwrecks in the 
Mediterranean (Foley 2008). One can only extrapolate what affects deep-sea fishing and 
other activities have had on shipwrecks worldwide (Soreide 2000; Coleman et al. 2003; 
Ballard 2007; Mindell 2007). Timing is another issue: archaeologists may encounter too 
much material to complete ground surveys in the time available to them, thus stressing 
the importance of training local officials to assist in visiting and recording sites. 

Chapter 8 explores these broader issues, focusing on what one can achieve with 
collected data. Certainly, looting is one of the main problems for archaeologists and her- 
itage preservation today. Ironically, widespread data-sharing regarding newly discovered 
ancient sites threatens those same sites by alerting looters to their presence. A modified 

Figure 1.7 Giza Pyramids, Egypt (scale 1:1440 m). Modern buildings have come within 0.26 
km of the Pyramids, 2008 image courtesy of Google Earth™ Pro. 



archaeological remote sensing ethics statement is suggested to augment site protection 
in such circumstances. In addition, educating the public about satellite remote sensing 
use in archaeology, including both implications and responsible application of sensing, 
is emphasized, especially since scientific ignorance and pseudoarchaeology often go hand 
in hand. This author and many colleagues receive diverse communications from individ- 
uals making claims about finding 'lost cities," "secret buried treasure," or "unlocking 
the secrets of the universe" using satellite imagery analysis or other techniques. This 
author has also received several invitations to find Atlantis from space. While often well- 
meaning and amusing, these proposals underscore the need for archaeologists, teachers, 
and the media to do a better job educating the public about new technologies and the 
need for good scientific practices in archaeology. The lack of an educational approach can 
muddy the waters of information dissemination, and allows the promotion of ideas that 
do not have a basis in scientific fact, even encouraging the nurturing of such ideas in 
even some serious entry level undergraduate students of archaeology (Cole et al. 1990). 

The conclusion (Chapter 9) will discuss the future directions for satellite remote 
sensing in archaeology. Here, it is emphasized that limited time and resources require 
the maintenance of a careful balance between landscape analysis, topographical study, 
and excavation. Placing archaeological sites in their proper contexts requires all three 
methods. As satellite technology improves and becomes more widely available, archaeol- 
ogists will be able to detect more subterranean sites, currently hidden from view. Shifting 
dunes in desert environments may reveal ancient fortifications, while desertification can 
obscure both well-known and undiscovered sites. This emphasizes the importance of 
satellite archaeology: Our ever-dynamic, ever-changing natural and human-engineered 
environments are causing more ancient sites to appear and disappear. The current global 
coverage by satellites represents the best technology already in place to reveal recorded 
and continuously refined levels of assessment for these ongoing, simultaneous changes 
in ancient and modern sites and landscapes. Archaeological satellite remote sensing 
bridges the gap between "dirt" archaeologists and remote sensing specialists by helping 
researchers to evaluate known archaeological sites in broader landscapes. 

The importance and timeliness of such a work is emphasized by the partial or total 
destruction of archaeological sites by numerous man-made and natural factors. This book 
also addresses an inherent bias in archaeological remote sensing: How much information 
is missed beneath the ground that current (declassified) satellite technology cannot 
detect? This is a question for every archaeological project in the world. This inherent 
bias must be recognized, what satellite remote sensing can detect and what it cannot. 
Just as in excavation, remote sensing research must be well-planned and well-executed, 
using a comprehensive methodology. The remote senser's eyes become trowels, peeling 
away the layers of multispectral imagery, while different image types act as sieves to 
detect smaller features. For the moment, what lies beneath the ground can only be 
reached through ground-based coring, selected geophysical surveys, or deep trenching. 
Ultimately one deals with only a percentage of the overall surviving evidence and biased 
preservation. Nonetheless, archaeological remote sensing already has the ability to detect 
tens of thousands of "missing" sites across the globe from the past millennia of human 
activity, and future advances in satellite remote sensing in archaeology will detect many 
thousands more. 





"Remote sensing," in its most basic definition, has existed in archaeology for as long as 
practicing archaeologists have lived, as it can be defined as a means to observe the sur- 
rounding landscape. Many ancient cultures used mountain peaks or desert cliffs to survey 
their landscapes prior to choosing the most advantageous positions for their temples, 
tombs, settlements, or other building projects. These early architects, priests/priestesses, 
and leaders did what most satellite remote sensing specialists do: Using landscapes as 
computer screens, they focused on the natural relationship of landscape features to 
potential places for living, burial, or worship. Each landscape had its own intrinsic 
layered meaning to past peoples, as their "multispectral" approach involved seeing 
these palimpsests as a unified whole. The field of remote sensing, as more narrowly 
defined, has only existed for the past 100 years, with the field of satellite archaeol- 
ogy appearing in the early 1970s. While all landscapes of archaeological interest are 
imbued with these layers of interest, satellite and other remote sensing specialists see 
what the naked eye cannot, in the hopes that such analysis will allow a glimpse into 
the true hidden nature of past places. Memory and meaning in landscapes are intangi- 
ble yet not unobtainable if one adopts a holistic approach to seeing otherwise invisible 

Such holistic approaches require all above-ground remote sensing specialists to use 
all appropriate sources of data, including balloons, kites, aerial photographs, and 
satellite imagery in their work. Aerial photography, to which the field of satellite 
archaeology owes much, has merited a number of books devoted to its usage, as 
described in Chapter 1. These books, written by career aerial archaeology specialists, 
discuss the history of aerial archaeology, and general techniques for interpreting this 
imagery in a wide number of contexts, shown in Figure 2.1. This discussion will 
not be repeated in detail here, where the focus will be on the archaeological uses of 
satellites over the past 30 years. However, a general historical background of aerial 
archaeology allows for the evaluation of the full trajectory of satellite remote sens- 
ing and its uses in archaeology. This includes how the field developed in relationship 
to satellite endeavors in remote sensing, NASA's space program, anthropology, and 
geography. A generalized history of archaeology is also not appropriate: The field 
of remote sensing has developed independently of archaeology, while the usage of 
satellites in archaeology cannot outwardly be associated with any particular theo- 
retical movement, although earlier technical developments seem to be connected to 



Figure 2.1 Orthophoto image of Washington, DC (scale 1:583 m), imagery courtesy of NASA 
World Wind. 

the "New Archaeology" scientific movement of the late 1960s-early 1970s (Renfrew 
et al. 1966). Remote sensing in archaeology is more closely connected with generalized 
developments of the field of remote sensing rather than archaeological trends over the 
past 35 years. 


A desire to view archaeological sites from the air, occurring just prior to and during 
World War I, essentially started the scientific field of remote sensing. Some of the ear- 
liest remote sensing in archaeology took place in the UK and Italy. The title of the 
"pioneer" of aerial archaeology belongs to UK army Lieutenant P.H. Sharpe, who took 
photographs of Salisbury Plain over Stonehenge in 1906, seen in Figure 2.2. Like many 
developments in archaeology, this was an accident: Winds had blown Lt. Sharpe off- 
course during an army exercise. The photographs, described by Colonel J. E. Clapper in 
Archaeologia, clearly show the stones in relationship to their surrounding earthworks 
(Capper 1907). Additional early aerial photos exist, taken from a balloon over the 
Tiber River, with images of Ostia Antiqua, and Venice from 1908, and of Pompeii 
in 1910 attributed to archaeologist Giacomo Boni (pers. comm., S. Campana and 
M. Forte, December 2006). Early photography taken for military purposes during 
World War I by Belgian pilots can even contribute to archaeology today (Stichelbaut 



Figure 2.2 Stonehenge, UK (scale 1:43 m), 2008 image courtesy of Google Earth™ Pro. 

Pilots flying over areas of natural and historical interest in the Middle East took 
a number of photographs with their personal cameras, and their superiors realized a 
significant military advantage when they viewed the photographs. Photos by taken 
by Obertendant Falk of the German Air Force in 1917 of Late Roman towns in the 
Negev were used by Theodore Wiegend in his book on Sinai (Wiegend 1920). The 
Bavarian State War archives in Munich have a number of archaeological photos of 
Israel and Jordan taken by Ottoman air force units who worked with German-Turkish 
Denkmalschutzkommandos . Under the Ottomans, there was a quick development of the 
use of aerial photographs. Royal Air Force flights assisted Alexander Kennedy's Petra 
work in 1923—4 (Kennedy 1925), Nelson Glueck's survey in Trans Jordan and Palestine 
in the late 1920s (Glueck 1965), Beazeley's early aerial photographs over different parts 
of Mesopotamia (Beazeley 1919, 1920), and O.G.S. Crawford's exploration of Iraq, 
Transjordan, and Palestine in 1928 (Crawford 1923a; Kennedy 2002), (See Figure 2.3). 
Crawford went on to found Antiquity, where he popularized the usage of aerial photog- 
raphy in archaeology as well as through more general publications such as the Christian 
Science Monitor (Crawford 1923b). Further uses of aerial photography occurred over the 
Transjordan (Rees 1929), various parts of the Middle East with famed aerial archaeologist 
Father Antoine Poidebard (Poidebard 1929, 1931, 1934), Sir Auriel Stein in Iraq and 
Transjordan during World War I and in 1938-9 (Stein 1919; Stein 1940), and in Egypt 
with Reginald Engelbach of the Royal Air Force (Engelbach 1929) and Gertrude Caton- 
Thompson in Kharga Oasis (who later did work with aerial photography in Zimbabwe) 
(Caton-Thompson 1929, 1931). Even Sir Mortimer Wheeler advocated the use of aerial 



2.3 Jerusalem (Dome of the Rock), Israel, note the detail of the roofing and structure 
(scale 1:88 m), 2008 image courtesy of Google Earth™ Pro. 

photography in India (Thakran 2000). Engelbach's work focused mainly on the pyra- 
mid fields and the archaeological sites of Luxor, but he lamented not being able to 
work elsewhere: 

For some reasons that will appear, I could have wished that flight has been over 
some of the Delta sites, but the experience was of interest since my department 
was anxious to ascertain whether any new surveys would be of value and whether, 
in certain of the old town-sites' tracks could be distinguished from the air which 
might reveal the situation of necropolis or other indications of interest far out 
in the desert. 

(Englebach, quoted in Rees 1929: 389-407) 

This statement shows, at this early time, the recognition of the advantage height 
could give to new archaeological discoveries as well as the importance of crop marks 
that could indicate the location of sites buried beneath modern fields. For exam- 
ple, oblique aerial photos taken by the RAF in 1932 above the floodplain near Tell 
el-Amarna, Middle Egypt, reveal a number of crop marks suggestive of buried walls (as 
interpreted by the author). Quite surprisingly, crop marks have never played an influ- 
ential role in the development of Egyptian archaeology (Parcak 2009). Cropmarks are 



not, however, a 20th-century idea. British antiquarian William Camden, in the 16th 
century, noted: 

But now has eras'd the very tracks and to teach us that cities dye as well as men, 
it is at this day a corn field, wherein the corn is grown up, one may observe the 
droughts of streets crossing one another, and such crossings they commonly call 
St. Augustine's cross. 

(Crawford and Keillor 1928: 37) 

A reference to "they" shows that people nearly 500 years ago acknowledged crop marks 
with a special term. One wonders if earlier groups would have recognized them as well. 
Aerial photography also played an important role in the archaeology of the Americas. 
Under the newly-appointed Director of the Carnegie Institution of Washington, Alfred 
Kidder, the Institution provided funding to support aerial reconnaissance missions by 
Charles Lindbergh and his wife in Central America to map known Mayan cities and 
record new features (Lindbergh 1929a, 1929b; see also Kidder 1929, 1930). Charles 
Lindberg and his wife photographed Chaco Canyon for Kidder, which aided in site 
visualization and comparison to the surrounding desert landscape. At other sites in the 
USA such as the Cahokia Mounds (Holley et al. 1993), shown in Figure 2.4, aerial 
photography played an important role in landscape interpretation (McKinley 1921). 
Sites difficult to reach in Peru proved much easier to visualize from space, thus setting 
the stage for broader aerial reconnaissance of the region (Johnson and Piatt 1930). 
Archaeologists also used early aerial photography for archaeological site management 

Figure 2.4 Cahokia Mounds, Illinois (scale 1:31 m), 2008 image courtesy of Google Earth™ Pro. 



and protection, during World War II, while German (Crashaw 2001; Going 2002), 
American, and British armed forces photographed a majority of Europe for military 
reconnaissance purposes. Some of these photographs are stored in archives, such as in the 
Smithsonian Institution in Green Park, Maryland, the Aerial Reconnaissance Archives in 
Edinburgh, and the JARIC-National Image Exploitation Centre archives in Brampton, 
UK, and on numerous European websites which provide archaeologists today with 
valuable historical snapshots of landscapes as they were in the mid-20th century, before 
the dramatic changes wrought by urban sprawl and mechanization of agriculture in the 
second half of the 20th century. 


During and following World War II, the number of aerial archaeology articles declined 
slightly, with a total of 73 publications between 1939—49, compared to 101 publications 
between 1928—38. This does not mean that World War II had a negative impact on 
the field of remote sensing: The very opposite is true. World War II had a large impact 
on archaeologists who learnt aerial photo interpretation skills working in intelligence 
during the War. In the early 1950s, more aerial archaeology scholarship was evident, 
with articles (not surprisingly) focusing on the parts of the world largely photographed 
during the aerial reconnaissance missions of World War II. This included Europe, the 
Americas, the Middle East, and the Far East (Goodchild 1950; Johnson 1950; Williams- 
Hunt 1950; Schaedel 1951), with a majority of publications focusing on the archaeology 
of the UK. These were as much based on new aerial photography, rather than using the 
older RAF photography. These publications started to address broader archaeological 
issues with the ongoing development of aerial archaeology methods. 1954 saw the first 
application of infrared (IR) photography in archaeology in Barbeau Creek Rock Shelter, 
Randolph County, North Carolina. Archaeologist J. Buettner-Januch compared the use 
of IR photography with normal film, and asserted that the photographed archaeological 
features appeared more clearly on the IR film. This was not without problems: The IR 
field needed variable exposure rates, so the general quality of the exposures could not 
be assessed prior to development. Buettner-Januch suggested that the IR field could 
be used for more aerial topographic surveys, but this would need to be done on a trial 
and error basis as scientists did not yet know the reflection and emission properties 
of ground objects (Buettner-Januch 1954). It was not until two years later that the 
first publication appeared that discussed the usage of IR film in aerial photography 
(Edeine 1956). 

Usage of aerial photographs remained fairly consistent through the mid-1950s, with 
further developments in to the scale of study, extending to landscapes, and an emphasis on 
interpreting and finding meaning in the data (Bradford 1956; Green 1957). Advances 
in spatial remote sensing from the mid- 1940s to the 1950s occurred with the V2 
rocket-launching scheme in New Mexico, at the White Sands Proving Ground, seen 
in Figure 2.5. Although this and other concurrent programs did not produce high- 
quality photographs, they showed the value of imagery taken from space (Lillesand et al. 
2004: 402). A decline in the usage of aerial photography in the USA occurred between 
1958—60, which appears closely connected to the diversion of government funding 
to scientific and nuclear research following the Soviet Union's launch of Sputnik in 
1957. Developing space and satellite technologies meant similar developments in space 



Figure 2.5 White Sands Proving Ground (scale 1:264 m), 2008 image courtesy of Google 
Earth™ Pro. 

photography. The Television and Infrared Observation Satellite (TIROS), launched in 
I960, showed meteorological patterns for the first time: Instead of just looking at clouds, 
scientists could now ponder the possibility of seeing through the Earth's atmosphere. 
Images from space taken by astronauts with handheld cameras also had applications 
to geological fields. The advent of the Cold War saw rapid development of the US 
space imaging program with the Corona, Argon, and Lanyard systems, with imagery 
taken from 1960-72. The Corona systems were termed KH-1, KH-2, KH-3, KH-4, 
KH-4A, and KH-4B, while the Argon system was designated KH-5 and the Lanyard 
system KH-6. The full impact of this program on archaeology would not be realized 
for another 2 5 years with the release of previously classified imagery, but the program 
contributed to the ongoing scientific programs involving global land coverage from 
space (Macdonald 1995). 

Publications appearing in the mid-late 1960s more frequently focused on the mul- 
tispectral capabilities of aerial photographs (Harp 1966; Agache 1968). With the first 
international colloquium on air archaeology in 1963 (Scollar 1963), aerial archaeology 
could be designated an "official" subfield in archaeology. While the Apollo 9 program 
made use of multispectral film (with black and white as well as color IR film) in 1969, 
archaeologists had started discussing its applications to landscapes impossible to visu- 
alize fully on normal film. Indeed, a review (Thompson 1967) of St. Joseph's (1966) 
The Uses of Air Photography demonstrated thinking some 15—20 years ahead of its time 
when the reviewer referred to electromagnetic remote sensing as "artificial satellites," 



and asked why scholars who had used it to study Earth resources had not considered 
electromagnetic remote sensing for archaeology. Thompson (1967) then suggested how 
the technology could be applied in rainforest research (Cantral 1975) with a ground 
resolution of between 1—200 feet. This was not put into practice until the work of Tom 
Sever and his NASA team in Central America nearly 30 years later (Sever 1995). 


IR imagery and a move beyond the visual part of the electromagnetic spectrum proved 
to be the foci for new developments in aerial archaeology between 1969—73. In the 
Little Colorado River in North-Central Arizona, aerial IR scanner imagery located par- 
allel linear features, which testing via soil and pollen studies showed to be prehistoric 
agricultural plots. Archaeologists could not recognize these features on the ground 
or from normal aerial photos (Gumerman and Schaber 1969). Studies on the spectral 
reflectance of soil (Condi t 1970) would eventually assist archaeologists attempting to 
use optical filtering (Chevallier et al. 1970) and IR photography (Gillion 1970) in 
their work. 

In 197 1 , George Gumerman and Theodore Lyons (197 1) published the breakthrough 
article on IR aerial archaeology and the potential for multispectral remote sensing in 
archaeology under the title of "Archaeological Methodology and Remote Sensing." This 
article moved the field of aerial archaeology beyond basic interpretation and identifica- 
tion to more automated pattern recognition, on which the field of satellite archaeology 
is largely based. After providing a basic overview of remote sensing, the first time 
such an overview could be found in an archaeological publication, the article discussed 
site location in the Hohokam region, where archaeological sites in the Chaco Canyon 
region of New Mexico dating to between 500 bc— 15 00 ad and prehistoric sites dating 
to 12,000—10,000 bc could be located. The area proved to be ideal for testing varying 
types of imagery, due to the relatively flat terrain and sparse ground cover. Infrared pho- 
tography allowed for clearer viewing of terrace walls and roads at the Chetro Ketl ruin 
in Chaco Canyon (Pouls et al. 1976), seen in Figures 2.6a and 2.6b, when compared to 
regular black and white aerial photographs. A chart provided by the authors compared 
color and black and white photography with IR black and white photography, color 
IR film, as well as multispectral photography, radar imagery, and microwave imagery. 
Categories in which the archaeologists compared each image type included exploration 
and identification, component analysis, identification soils, rock types, soil moisture, 
vegetation, vegetation pattern analysis, plant vigor identification, geomorphology, and 
land use analysis. According to the team, only color IR film and multispectral pho- 
tography allowed archaeologists to examine all of the categories (Gumerman and 
Lyons 1971). 

This early article was not only valuable for providing an overview of the basic appli- 
cations of IR imagery to archaeology, but also for discussing broader issues still relevant 
in satellite archaeology today. Already, cost proved to be a factor in the utilization 
of some imagery types, especially scanner imagery, which allowed for the identifica- 
tion of prehistoric soils from the Egyptian Delta via night readings. The article noted 
that buried cultural features absorb or emit radiation differently than the surround- 
ing soil, with prehistoric garden plots appearing best in color IR film. Even at that 
time, the authors recognized the importance of ground-based survey as well as using as 



Figure 2.6 Chaco Canyon, New Mexico: (a) Orthophoto (scale 1:110 m); and (b) Quickbird 
imagery (scale 1:100 m), 2008 images courtesy of Google Earth™ Pro and NASA 
World Wind. 

broad a range of imagery types from different seasons. They pointed out (and this 
remains true) that there is not one single type of remote sensing device on which 
archaeological prospecting should rely, since each archaeological site will have their 
own spectral properties. Radar imagery, mentioned for the first time with regards to 
its potential archaeological applications, was too poor in resolution to be used more 

Prior to the development of multispectral satellite imagery, archaeologists were 
already thinking of the advantages and disadvantages of aerial photography for broader 
archaeological survey (see Figure 2.7). With theoretical and general archaeological infer- 
ence, archaeology was beginning to be thought of as a science (Watson 1976), which 
also aided the transition to the wider-spread application of multispectral imagery. Color 
IR film helped archaeologists to identify microenvironmental zones as well as alluvial 
valley centers and terraces in the Tehuacan Valley, Mexico (Gumerman and Neely 1972), 
showing the importance of considering different archaeological landscapes in the same 
imagery. Additional studies (Lyons et al. 1972; Tartaglia 1973; Harp 1975; Ebert 1976; 
Jorde and Bertram 1976) dealt with a wide range of archaeological issues (Ebert et al. 
1979), moving aerial archaeology beyond mere site discovery to a problem-oriented 
field. The journal Aerial Archaeology first appeared in 1975, and attracted international 
attention while focusing mainly on studies from the UK (Edwards and Partridge 1977). 
In a review of the Impact of Natural Sciences on Archaeology, scientists presented the 
idea of using the electromagenetic spectrum for aerial research (Blank 1973). The idea of 
analyzing imagery for archaeology on a computer had not yet become a possibility. Pro- 
ducing "true -plan" mapping of archaeological features from oblique aerial photographs 
on a computer had been discussed (Palmer 1978), but the cost of computers was, at the 
time, beyond the means of most archaeologists. The launch of the Landsat program in 



Figure 2. 7 Celone Valley, Italy (scale 1:336 m), note cropmarks in the fields, 2008 image courtesy 
of Google Earth™ Pro. 

1972 would soon change how archaeologists thought of applying imagery to their work 
(Hamlin 1977). 

While the Apollo program was terminated in 1972, that year marked the beginning 
of the Landsat program, a image of which appears in Figure 2.8 (Wiseman and El-Baz 
2007). The US Department of the Interior, in 1967, started the Earth Resources Technol- 
ogy satellites (ERTs), with the ultimate goal of gaining unsupervised and multispectral 
information about the Earth's surface. Launched in 1972 as ERTS-I with an orbit of 
900 km, a resolution of 80 m and a revisit time of 18 days, the USA invited scientists 
from around the world to study the collected data, which was renamed the "Landsat" 
program in 1975. The USA has launched a total of six Landsat satellites (Landsat 6 
failed at launch). Geology produced some of the earliest Landsat studies applicable to 
archaeology, with scientists using the imagery to study landscape geomorphology and 
to map large desert regions in Egypt (Moussa 1977; El-Etr et al. 1979; Khawaga 1979; 
El-Rakaiby et al. 1994). France started another multispectral satellite progect in 1978, 
called Systeme Pour l'Observation de Terre, or SPOT, which was launched in 1986. 
SPOT was the first satellite that could achieve stereoscopic imaging. Unlike the quick 
rise of interest in Landsat, SPOT did not prove popular with archaeologists carrying out 
remote sensing work until 10 years after its launch. 

Archaeologists also considered the larger implications of archaeological remote sens- 
ing applications (Snow 1979). How archaeologists might consider remote sensing 
specialists as members of their team still remains a subject not well discussed (Rapp 
1975). Why more archaeological applications of aerial photography could not be found 
in the USA also became as issue. Concentrated application of aerial photography to 



Figure 2.8 Ellis Island, New York City, New York, coarse resolution Landsat image 
(scale 1:180 m), note the pixel sizes and that the details of the imager are not possible 
to view, 1980 image courtesy of NASA. 

the archaeology of the UK appeared to be expected. While UK archaeology could be 
seen as geological and historical in nature, the archaeology of the USA was viewed 
as more anthropological with a Native American focus. Issues involving bias and over- 
interpretation of remote sensing data were also important. Archaeologists placed an early 
emphasis on more methodical imagery analysis rather than focusing on surveying tech- 
niques (Ebert et al. 1979). Issues with archaeological sampling also contributed towards 
the development of archaeological remote sensing. In a prehistoric site study conducted 
in the Blue Ridge Mountains in Shenandoah National Park (Figure 2.9), archaeologists 
recognized the difficulties of searching for sites over an area covering 180,000 acres and 
the issue of sites not being evenly distributed across Earth's surface. They addressed the 
problem of getting a representative sample of sites by using geology and landscape geo- 
morphology, and the extent to which IR photography could contribute to archaeological 
site location (Ebert and Gutierrez 1979). 


The early 1980s saw some of the earliest application of multispectral Landsat satellites 
in archaeology with accompanying survey strategies (Campbell 1981; Drager 1983; 
Hardy 1983; Hoffman 1983; Parrington 1983). Although Landsat had an 80 m resolu- 
tion and archaeologists saw the potential of applying Landsat satellite imagery for more 
economic site surveys, they recognized the need for much higher resolution satellite 



Figure 2. 9 Blue Ridge Mountains in Shenandoah National Park, the detail of the mountain 
terrain can be altered in Google Earth™ Pro (scale 1:757 m), 2008 image courtesy 
of Google Earth™ Pro. 

imagery. An early study in Libya made use of the Landsat imagery (Figure 2.10) by 
dividing the survey area into facets with recognizable landforms, in order to compare 
past and present land usage, and analyze livestock and human carrying capacities. The 
team advocated that the imagery subdivided the landscape so they could have more 
effective sampling strategies. Terrain types included plateaus, uplands, colluvial slopes, 
aeolian sand and gravel deposits, fluvial sand and gravel deposits, upper colluvial slopes, 
and wadi deposits, with the land divided in three categories: Upland areas not suitable 
for farming or settlement, colluvial slopes suitable for settlement but not farming, and 
alluvial wadis suitable for farming but not settlement. The land facet maps showed that 
54 percent of known archaeological sites could be found in alluvial wadis suitable for 
farming and not settlement, yet this landscape type only covered 23 percent of the total 
survey area (Allan and Richards 1983). This is, however, a cyclical argument, as it is 
based on a known distribution of sites, which is almost certainly biased. 

Early applications of radar imagery in Belize and Guatemala allowed archaeologists 
to address questions relating to Mayan economic structure. With the Maya lowlands 
measuring more than 250,000 km and being covered in dense vegetation, archaeol- 
ogists needed a way to see beneath the thick canopy. The distribution of ancient sites 
was not well understood, and with 300 known Mayan centers, archaeologists assumed a 
number of ancient features remained to be discovered. Using airborne side-looking radar 
(initially developed as a method to map the surface of Venus) mounted on a CV-990 
airplane, archaeologists took photos during 1977, 1978, and 1980. After examining the 



Figure 2.10 Landsat image of Libya (scale 1:1248 km), image courtesy of NASA World Wind. 

data via light transparencies, the team noted grid patterns that were likely to repre- 
sent Mayan canals. A number of non-archaeological features appeared as well, including 
modern roads and logging trails, so the team required detailed ground survey to confirm 
the features. With so many of the archaeological features observed during the ground 
survey being too small to show at the minimum radar resolution of 1 5 m, archaeologists 
recognized that future seasons would require higher resolution imagery. The minimum 
and maximum areas of late classic canal systems were estimated based on the total 
area of agricultural drainage. As a result, the team achieved a far better understand- 
ing of southern lowland classic Mayan uses of intensive agriculture and insights into 
Mayan economic structure (Adams et al. 1981; Adams and Wood 1982). Anthropologi- 
cal issues relating to the application of remote sensing also became apparent (Fedick and 
Ford 1990; Willey 1990; Folan et al. 1995; Fedick et al. 2000). Anthropologists could 
now utilize remote sensing to address cultural gaps across time and space through the 
examination of demographic changes, settlement patterns, and housing. Archaeologists 
also now advocated a move beyond the salvaging of sites to the evaluation of landscapes 
necessary to conceptualize past behavior (Posnansky 1982). 

The early remote sensing work of Farouk El-Baz, now the Director of the Center for 
Remote Sensing at Boston University, contributed to the development of archaeological 
remote sensing. Expeditions to the Egyptian western desert verified patterns seen from 
Gemini and Apollo-Soyuz photographs, as well as Landsat imagery. Dune encroachment, 
now a major environmental and conservation problem, was noted at this early date 
(El-Baz 1980). The total area of reclaimed desert in Egypt between 1965 and 1975 



Figure 2.11 Land reclamation in Middle Egypt (scale 1:3413 m), note the areas being extended 
to the West for future development, 2008 image courtesy of Google Earth™ Pro. 

could be calculated from space as being 1000 km , which is still increasing today 
(Figure 2.11) (El-Baz 1984). Human impact on the landscape was also examined by 
El-Baz with observed changes in water levels within Lake Nasser. Construction of the 
Aswan high dam aided the prevention of major damage from higher water levels recorded 
from 1973—84. From 1981—4, significant falls in the general water levels were noted in 
Lake Nasser (El-Baz 1989), perhaps indicating a drier period. El-Baz has spent the past 
30 years advocating the use of satellites to map patterns of human behavior, in particular 
in relationship to changing environments. 

Nothing had more of an effect on the direction of satellite archaeology in the mid- 
1980s than the first conference on Remote Sensing in Archaeology. Held in 1984, it 
was sponsored by NASA and organized by Tom Sever and James Wiseman; and called 
"Remote sensing and archaeology: Potential for the future." Taking place at Stennis 
Flight Center's National Space Technology Earth Resources Laboratory in Mississippi, 
22 leading archaeologists attended several days of presentations and discussions on the 
applications of remote sensing to archaeology. The scientific implications of this confer- 
ence cannot be understated, and support for the conference, with funding from NASA, 
NSF, and National Geographic, shows how much importance was attached to its implica- 
tions for archaeological research. At the conference, NASA scientists and archaeologists 
presented the results of ongoing research at Poverty Point, Chaco Canyon, in addi- 
tion to providing a comprehensive overview of remote sensing, various satellite types 



(including Landsat, SIR- A, TIMS, and LIDAR), and potential applications to archaeol- 
ogy. Archaeologists were invited by NASA's Jet Propulsion laboratory to examine the 
flight path of the SIR-B RADAR, to see if it coincided with their research area (Sever 
1985; Sever and Wiseman 1985). This meeting resulted in several significant long-term 
archaeological partnerships: For example, NASA archaeologist Tom Sever joined Pay son 
Sheets' research team in Costa Rica to examine the use of remote sensing in tropical envi- 
ronments. This would lead to the testing of thermal data from TIMS, 2-band SAR, and 
Landsat data in the Arenal region (Sheets and Sever 1988). In the report stemming from 
the NASA meeting, Tom Sever noted: 

New technologies to which they were introduced may represent the kind of 
scientific breakthrough for archaeology in the second half of the 20th century 
that radiocarbon dating was in the first half of the century . . . advancements in 
these areas are occurring so fast that unless archaeologists apprise themselves of 
the technology now, they will be unable to keep pace with the technology in 
the near future. Moreover, they may find that developers and pot hunters have 
once again beaten them to the field. 

(Sever and Wiseman 1985: 2-11) 

Some 25 years later, these words continue to ring true. 

Ongoing work in Europe, the USA, and the Middle East continued to show the 
applications of satellites to archaeology. The First International Conference on Remote 
Sensing and Cartography in Archaeology was held 1983, where the establishment of 
a European remote sensing center in Strasbourg was announced (now known as the 
European Space Agency). Work in Morton Fen, Lincolnshire, UK (Figure 2.12) proved 
the feasibility of using Landsat imagery in wetland environments for site predictive 
modeling (Donoghue and Shennan 1987; Donoghue and Shennan 1988). Aerial ther- 
mography in Leksand, Dalecarlia, central Sweden showed buried walls and foundations 
during the mapping of the top layer of ground (Lunden 1985). In the USA, Landsat 
satellite imagery aided in predictive modeling in the Delaware Plains regions, with the 
team considering regional combinations of variables rather than exact site locations to 
great success (Custer et al. 1986). Even larger applications of satellite imagery were yet 
to be realized, as maps of Syria-Palestine (Cleave 1985) were created from seven Landsat 
images and aided in a UNESCO-funded survey project in Libya(Dorsett et al. 1984). The 
SIR-A Synthetic Aperture Radar Mission, flown in 1981 to support terrestrial geologic 
mapping over the Western Sahara desert, detected the so-called "radar rivers," or former 
riverbeds beneath the sand of the Sahara, viewed in Figure 2.13. A survey in a 6 x 
1 5 km area along the edges of the river system found sites dating to the Middle-Late 
Neolithic, which archaeologists claimed to be temporary settlements. The team, led 
by Fred Wendorf, saw the "radar rivers" as bedrock basins dug out by wind, which 
would have collected soils and water during a more arid periods. Thus, they would have 
supported more seasonal occupations (Wendorf et al. 1987). Other scientists suggested 
these were rivers with long-term occupation, and that Wendorf did not survey in an 
appropriate area in relationship to the area exposed in the SIR-A imagery. Excavations 
revealed material from roughly 200,000 years ago, which the team suggests as the time 
the rivers dried up to smaller watering holes (McHugh et al. 1988, 1989). Satellite 
imagery played a critical role in the initial archaeological analysis, and allowed for the 



Figure 2.12 Landsat image of Morton Fen, Linconshire, England (scale 1 :609 m), image courtesy 
of NASA World Wind. 

first major archaeological debate over the meaning of remote sensing results. Work with 
future satellite missions will help to shed light on the occupation sequences in Egypt's 
Western Desert. 


In the late 1980s, major gaps in the utilization of GIS and remote sensing could be 
traced to a lack of funding and training, yet archaeologists recognized their potential 
(Fagan 1989; Myers 1989; Wiseman 1989). However, the early 1990s saw a change in 
this respect. The overall percentage of archaeological remote sensing articles would grow 
as well: 10 percent of articles cited in this volume date to the 1980s, while 30 percent 
date to the 1990s. In the first published overview of the use of satellites in archaeology 
(Limp 1989), the authors pointed out a nine-year gap in publications, due to a reluctance 
to embrace the new technology. While the book provided a detailed overview in three 
chapters of how multispectral satellite imagery analysis works, a chapter on ongoing 
remote sensing work in the Ozarks (a mountain range located in Missouri, USA), as 
well as a chapter on GIS and remote sensing integration, the book criticized a number 
of publications for doing only visual interpretation. Visual detection still continues to 
contribute immeasurably to the fields of aerial archaeology and satellite archaeology. 
The authors stressed usage of satellite technology for the detection of long-term sites, 
but stated the technology could not be used for locating short-term camps. The authors 
also emphasized locating transportation and communication networks in addition to the 



Figure 2.13 Kharga Oasis, Egypt (scale 1:6192 m), where the ancient river systems can be seen 
using Quickbird satellite imagery, 2008 image courtesy of Google Earth™ Pro. 

environmental zones other projects had identified (Lorralde 1991). Other archaeologists 
recognized the importance of integrated usage of GIS and remote sensing (Madry and 
Crumley 1990), but training was still an issue. 

A critical topic discussed by the authors was the lack of literature on predictive 
modeling. Flaws in the general idea of predictive site modeling were noted in a 1993 
review of GIS in archaeology. Ultimately different models and techniques are needed to 
predict the location of sites in different environments (Davis 1993). As discussed by early 
remote sensers, a wide range of imagery types should be utilized in research. Additionally, 
there is no "one size fits all" approach to predictive site modeling: One would not 
necessarily run the same algorithms to detect sites in the desert as for sites in a rainforest. 
Was this the practice employed with early remote sensing in archaeology? As satellite 
archaeology evolved as a scientific practice, it has shown archaeological sites appear 
in locations that relate to landscape geomorphology, weather patterns, and historical 
changes. Remote sensers do not predict where archaeological sites will appear based 
solely on mathematical models: They also use historical data, much of it based/building 
on the pure visual analysis referenced above. Working in a mathematical vacuum has 
and always will be dangerous for satellite archaeologists, which underscores the need to 
use as much data as possible, evident from the very conception of the field. 

Satellite archaeology started to attract the attention of the global media, with a 1992 
article in the New Scientist discussing ongoing archaeological remote sensing projects. 



These projects included NASA archaeologist Tom Severs work in New Mexico and 
Costa Rica. Sever's work found that Landsat could be too coarse for most archaeological 
work, while Thematic Mapper simulators and the TIMS jet over Chaco Canyon, in NW 
New Mexico, revealed many previously unknown walls, roads, and other features, as well 
as footpaths in Costa Rica (Sheets and Sever 1991). This work showed applications of 
remote sensing in both humid and dry climates, and proved that the timing of imagery 
could be an issue. By 10 a.m., the roads in Chaco Canyon could not be distinguished 
from the surrounding terrain due to heat. Another project mentioned in the article was 
that of archaeologist Frederick Cooper, who found sites in the Western Peloponnesus 
of Greece (Figure 2.14). He used imagery that connected richer vegetation with scrub 
oak atop archaeological features (Joyce et al. 1992). Now archaeologists realized that 
satellites could not only be used to locate sites, but could be used to find vegetation 
that predicted site locations. Earlier concerns relating to larger climatic issues were also 
beginning to be explored. Reports appeared from other parts of the world: Compar- 
ing Landsat imagery to aerial photography in Thailand showed how well the satellites 
detected moated sites. Imagery only detected 75 percent of mounds and 23 percent 
canals (Parry 1992), but the satellite imagery could bridge the gap between predictive 
site modeling and archaeological site detection. Locating environmental features that 
could forecast archaeological site locations proved useful in Cumbria, UK, where Land- 
sat imagery located areas of peat (Cox 1992). Landsat satellites also played a role in 
not only detecting archaeological sites, but also in examining overall prehistoric water 
management strategies (Kidder and Saucier 1991). 

The application of satellite imagery to the archaeology of specific regions, focusing on 
more anthropological applications (Conant 1990), started in the mid-1990s (McGovern 

Figure 2.14 Landsat image of Western Peloponnesus of Greece (scale 1:5300 m), image courtesy 
of NASA World Wind. 



1995; Deo and Joglekar 1996). The book Ancient road networks and settlement hierarchies 
in the New World (Dubois 1996) had two studies focusing on applied remote sensing 
studies of ancient transportation and religious uses of the landscape (Sever and Wagner 
1991; Sheets and Sever 1991). Additional reviews then appeared discussing applications 
of satellites to archaeology (Palmer 1993), with the first set of collected reports focusing 
on remote sensing appearing in the book Applications of Space-age Technology in Archaeology 
(Behrens and Sever 1991). Remote sensing was also making its way into general books 
on archaeological science (Tykot 1994), which, along with the New Scientist article, 
encouraged the more widespread usage of the technology. 

Global media attention was also given to the discovery of the "lost" city of Ubar, 
located through a combination of SIR-A imagery, ground survey, and excavation (Fiennes 
1991; Blom 1992). SPOT satellite imagery also started to play more of a role in archaeo- 
logical site detection (Brewer et al. 1996; Guy 1993). Broader usage of satellite imagery 
also included using RADAR to map Angkor Wat (Anon. 1995), Landsat imagery to 
map prehistoric canal systems in Arizona (Showater 1993) as well as combining geolog- 
ical predictive modeling with archaeological feature location (Carr and Turner 1996). 
Satellite archaeology publications remained consistent throughout the 1990s, with an 
average of 10— 14 publications per year appearing in peer- reviewed archaeology journals 
or reference volumes. The launch of 5 m resolution SPOT 4 in 1998, IRS-1C (India's 
remote sensing program) in 1995, Landsat 7 in 1999 with enhanced thematic mapper 
capabilities, and the 1 m resolution IKONOS satellite opened the door for even more 
applications in archaeology. At this time, some archaeologists lamented the overall lack 
of aerial archaeology, claiming that it was being largely neglected as a major survey tool 
(Palmer 1995). In the 1970s, aerial archaeology aided in the process of survey for site 
locations, and there was tremendous growth in the field of aerial archaeology from the 
mid 1980s to the present. This complimented the increase of satellite remote sensing in 
archaeology from the same time period. This does not take away from the importance 
of aerial photographs for archaeological work: They should be used in conjunction with 
satellites for the best overall results. 

2000 onwards 

From the late 1990s onwards, satellite archaeology has become even more prevalent in 
archaeological publications (Mahanta 1999), press coverage, conferences, grant applica- 
tions, and courses, whether through individual classes or overall programs on landscape 
archaeology. Through the support of international organizations such as UNESCO, 
countries are being encouraged to consider the additional usage of satellite in preserving 
their cultural and natural heritage. UNESCO's remote sensing programs (directed by 
Mario Hernandez) support non-invasive techniques for heritage conservation. Via various 
space technologies, UNESCO conserves and monitors natural and cultural listed sites. 
UNESCO's remote sensing office has 24 international agencies and 16 universities or 
research institutes as partners, and also supports education and training in remote sens- 
ing to children and adults worldwide. In 2000, the project "Culture 2000 — European 
Landscapes: Past, Present, and Future," sponsored by English Heritage, Belgium, 
Germany, Italy, and Hungary, started promoting archaeological field schools, the use 
of aerial and ground-based surveys for archaeological investigation, outreach, and addi- 
tional fieldwork (Bewley and Musson 2006), thought the active use of satellite imagery 



was only a very minor component of this project, focusing as it did largely on "traditional" 
aerial archaeology. This was followed, in 2001, by the "Open initiative on the use of 
space technologies to monitor natural and cultural heritage of UNESCO sites," a cooper- 
ation framework to manage, monitor, and observe, formed of the International Working 
Group of Space Technologies for World Heritage, including UNESCO, NR-ITABC 
(Italy), GORS (Syria), the Chinese Academy of Sciences, NASA, ETH (Switzerland), 
and others. 

Conferences in the 1990s and 2000s have featured additional panels or subsections 
devoted to the use of remote sensing in archaeology, with an increase in the number of 
outlets for research papers over the 10 years. Panels involving remote sensing have been 
a part of the Society for American Archaeology and American Institute of Archaeology 
conferences for over 10 years, while the Computing Applications in Archaeology (CAA) 
conference has long supported GIS and remote sensing papers. The American Schools for 
Oriental Research (ASOR) conference first supported papers in a panel on "Geograph- 
ical Information Systems (GIS) and archaeology," which is now called "Geographic 
Information Systems (GIS), Remote Sensing, and Archaeology." The first international 
conference on remote sensing in archaeology, held at Boston University in 1998, was 
described in Archaeology magazine (Wiseman 1998). Now, papers on satellite archaeology 
are an accepted part of most archaeology conferences. 

China organized the next major satellite archaeology conference in 2004, with the first 
International Conference for Remote Sensing Archaeology, attended by over 100 interna- 
tional scholars (Figure 2.15). China can claim the most comprehensive satellite archaeol- 
ogy program in the world (Banks 1995). Under the National Center for Remote Sensing, 
there are 10 Chinese regional centers for remote sensing in archaeology, supported by the 
Chinese Academy of Sciences and the National Remote Sensing Center of China (Jingjing 
2006). China's developing space program has contributed to this support: China has 
launched 50 satellites from five spacecraft as part of the Chinese space program. The 2004 
conference showed the international participants the cutting-edge research being done 
by a group of well- trained and motivated scholars. The overall purpose of remote sensing 
in China is to integrate the natural and humanistic sciences. The Center for Remote Sens- 
ing Archaeology, located in Beijing beside the 2008 Olympic complex, and founded 
in 2001, hosts a number of computers and state of the art 3D visualization systems. 
Students and professional Chinese archaeologists receive extensive training in the appli- 
cation of remote sensing for archaeological site detection, preservation, and protection 
(JAuetal. 2002). 

In fact, the United Nations recently approved the National Center for Remote Sens- 
ing in China to be a major regional center on remote sensing and heritage, showing the 
global recognition of China's research on satellite archaeology. The first Chinese general 
conference on remote sensing in archaeology was held in Beijing in 2002, with dis- 
cussions of remote sensing on the Qinshihuang Mausoleum as well as other lesser, but 
no less valuable, archaeological sites throughout China, including cities along the Silk 
Road. The Joint Laboratory of Remote Sensing Archaeology did not apply aerial pho- 
tographs to archaeology in China until the mid-1990s, but found that historical images 
could be invaluable for comparing past and present landscapes (Huadong and Changlin 
2004). For example, aerial photographs from the 1960s around Sanmenxia Reservoir 
(Figure 2.16) showed how significant modern development has affected past remains 
(Tan et al. 2006). Chinese archaeologists have applied a wide range of remote sensing 



Figure 2.15 Photograph of the International Conference on Remote Sensing Archaeology, image 
by Sarah Parcak. 

to their research, including hyperspectral imagery, high-resolution data, and varying 
image types. Teams have focused on the Shangqui area, using old maps and black and 
white aerial photographs to locate old water transportation routes, canals, locks, and 
various relic landscapes (Li et al. 2004). 

Remote sensing training schools held in Europe are teaching the next generation of 
landscape archaeology specialists through a comprehensive series on hands-on classes 
and fieldwork. Stemming from the Second International Conference on Remote Sensing 
in Archaeology, intensive field schools in Italy provide training in archaeological sur- 
vey, GIS, remote sensing, and all issues relating to landscape archaeology analyses and 
landscape reconstruction (Campana and Forte 2006). The International Space University, 
active for over 20 years in Strasbourg, France, also encourages students to consider space- 
based archaeological applications. In the summer 2007 session, ISU students produced 
START: Space Tools Supporting Archaeological Research and Task, which had case studies 
from the Middle East, Central America, and South America discussing how satellite 
archaeology could be cost-beneficial to general archaeological survey and issues relating 
to landscape reconstruction. 

In the past seven years, additional high resolution satellites, such as Quickbird, and 
hyperspectral satellites, such as the EO-Hyperion, have appeared on the market with 
significant implications for archaeological site detection. Nearly 300 publications have 
appeared since 2000 with satellite archaeology as their primary focus. Now, archaeolo- 
gists are more likely to see remote sensing and GIS in university and cultural resource 
management (CRM) advertisements. As a result, many PhD students have taken an 
interest in the subject area. Although this chapter has provided an overview of the 



Figure 2.16 Sanmenxia Reservoir, China (scale 1:2166 m) note significant urbanization, 2008 
image courtesy of Google Earth™ Pro. 

overall advances of remote sensing in archaeology in relationship to the field of aerial 
photography, their differences and similarities should be emphasized. Aerial photogra- 
phy still plays an important role in archaeology in countries where aerial photography 
is permitted (Lemmens et al. 1993; Fassbinder and Hetu 1997; Doneus 2001). 

Many examples of its use can be found in the UK (Bewley 2001; Halkon 2006; 
Deegan and Foard 2008), France (Bezori et al. 2002), Italy (Campana et al. 2006; 
Bohemia (Godja 2004), El Salvador (Fowler et al. 2007), China (Zhang and 
Wu 2006), Syria (Weiss 1991), Israel (Riley 1992), Egypt (Zurawski 1963; 
Vercoutter 1976), Australia (Connah and Jones 1983), and Armenia (Hakobyan and 
Palmer 2002). 

Jordan, in common with much of the Middle East, has extensive historic aerial 
photography, and more recently, with the support of the royal family, extensive aerial 
reconnaissance and oblique aerial photography has been taken of the country specifically 
for archaeological purposes (Kennedy and Bewley 2004). In other projects archaeolo- 
gists have used aerial photography to examine water management strategies (Kennedy 
1995) and discover early Roman period roads (Kennedy 1997). In the USA, aerial pho- 
tography archives exist in each state, but are generally accessed through each state's 
forestry division. The AmericaView program ( has connected 
the digital and photographic archives of each state, making it possible for archae- 
ologists in the USA to have access to a broad range of free satellite imagery and 



Figure 2.17 Boston, Massachusetts orthophoto (scale 1:3456 m), imagery courtesy of NASA 
World Wind. 

aerial photographs. These images are also viewable on NASA's World Wind program 
(Figure 2.17). 

Examining how aerial photography and satellite archaeology can be used together 
is a topic not broached regularly by archaeologists, and belongs in a chapter on 
the history of satellite archaeology (and is also discussed in chapter 4). Without 
aerial photography, satellite archaeology would simply not exist. Each has advan- 
tages and disadvantages, which should be discussed, with the ultimate aim of using 
them together for archaeological analysis. Aerial photography has become a subspe- 
cialization within archaeology, just like satellite archaeology. Both require specialized 
training. Archaeologists, as a whole, still rely primarily on visible means to iden- 
tify archaeological sites with aerial photographs and satellite images. This visible 
aspect of satellite images should not be overlooked, as it can allow the creation of 
maps in areas where they do not exist and make it possible to search for features 
over large tracts of land before any survey season. Satellite imagery can have a full 
range of the electromagnetic spectrum, thus making them valuable to use in tan- 
dem with aerial photographs. Both fall within the broader category of archaeological 
remote sensing, and are two halves of the same coin. It should be noted that LIDAR, 
TIMS, and other airplane-based imaging systems are not considered aerial photographs, 
although some of the issues affecting aerial photography quality can affect their 
imaging capabilities. 

The similarities of the two landscape visualization techniques provide a good starting 
point. Both are in use by archaeologists today, and can contribute immeasurably towards 
the detection of previously unknown archaeological sites and features. Without prior 
training and ground experience, it becomes easy to misinterpret both sets of data, as 



well as "see" something that may or may not exist. Experienced remote sensers and aerial 
photography specialists will rely on previous studies and maps to determine potential 
features, especially if an anomaly occurs outside a known archaeological landscape. Cloud 
cover can inhibit each as well. Without good ground control points or maps, it is not 
possible to rectify or georeference imagery. Scale of data is important in each instance. 
Ground- truthing to locate major archaeological features (such as entire sites, large fea- 
tures such as barrows, etc.) may be different than ground-truthing for smaller features 
(such as walls) where excavation may be necessary. Everything depends on the presence 
or absence of material culture remains in the ground. 

With aerial photography, the spectral resolution may be limited, yet the spatial res- 
olution can be as good as 0.1 m (Quickbird high-resolution satellite imagery only has 
0.6 m resolution). In aerial photographs, archaeologists can view aerial photographs in 
the visible and IR part of the electromagnetic spectrum, with the IR range of pho- 
tographs in the 700—900 micron range of the spectrum, called near-infrared, or NIR). 
Satellite imagery provides data within more narrow parts of the IR, which may be useful 
in determining specific vegetation types. Importing aerial photographs into a GIS along 
with analyzed satellite imagery will allow archaeologists to have access to the highest 
resolution imagery as well as the non- visible part of the spectrum. Valuable comparisons 
can be made between features viewable on satellite imagery and on aerial photographs, 
seen in Figures 2.18a and 2.18b. Perhaps the features cannot be seen on visible Quick- 
bird imagery, yet an unusual spectral signature appears, thus revealing the same ancient 
feature as a higher resolution aerial photograph. 

Imagery quality is another topic archaeologists need to consider when using aerial 
photographs or satellite imagery. When any individual takes photos with film, they are 
in analog format, or providing a physical representation of an image. In order to view 
the imagery on a computer screen, the imagery needs to be converted to digital format. 
There are two ways to convert from analog to digital, either through scanning the print 
of the film, or the negative. Any time data is one step removed from its original format, 
quality is lost, so it is recommended that the scans be done from the negative. It is 
becoming increasingly rare for film processing labs to have the expertise to develop IR 
film. This has changed with the advent of digital cameras, which now have the ability 
to take photos in the IR range. These cameras will aid aerial archaeology immeasurably, 
especially as their spectral range increases. 

Cost is another factor when using aerial photography and satellite imagery. While 
many satellite images are free online, costs can range up to many thousands of dollars. 
Going to an archive to copy aerial photographs can be relatively inexpensive, while 
many aerial photos can be found on free online archives. A good computer and remote 
sensing software are required to do satellite imagery analysis, in particular a computer 
with a high-end processor for dealing with larger satellite imagery datasets. Both aerial 
photographs and satellite images can also require significant storage space. Licenses 
for remote sensing software can cost thousands of dollars, although free programs are 
available online for downloading with more limited processing functions. 

Getting the exact imagery needed for project work can also be a factor. With 
higher- resolution satellite imagery, if the funding is in place, one can specify the exact 
coordinates to be imaged. Aerial photography, using digital photos, can also image a 
specific area, but can have problems associated with weather. If the conditions are poor, 



Figure 2.18 The Alamo, Texas: (a) Orthophoto (1:45 m); and (b) Quickbird imagery 
(scale 1:61 m), 2008 image courtesy of Google Earth™ Pro and NASA World 



the plane cannot take off, or the pitch of the plane can be affected by winds that may 
not be evident on the ground. An archaeological team may need an image from an exact 
time of day to get the right lighting, which is easier to achieve with aerial photography. 
Satellite images are consistent in that each image of an area is taken at the same time of 
day. This aids with archaeological work using high-resolution Quickbird imagery, which 
is taken at 10.30 a.m. each morning. If the lighting is not correct, a team can control 
sunshade on imagery analysis programs. Varying factors affect imagery quality: Wind or 
camera issues can affect the quality of aerial photographs, while technical problems can 
affect the quality of satellite imagery. This happened in 2003 with Landsat imagery being 
striped, which can be seen in Figure 2.19- One relative advantage of aerial photography 
is its ability to test varying film types at different heights to compare results, as Gwil 
Owen (1993) has tested at Tell el-Amarna in Middle Egypt using a balloon. Other 
projects have used kites (Rossi and Ikram 2002), helicopters (Eisenbeiss et al. 2005, 
2006; Eisenbeiss and Zhang 2006), or powered parachutes (Hailey 2005) with great 

Every archaeological project will have different needs, yet there are few archaeo- 
logical projects that could not benefit from using both satellite imagery and aerial 
photographs. Aerial photographs provide a historical landscape perspective, which the 
satellite imagery allows archaeologists to see additional features buried by soil, covered 
by vegetation, or partially to completely obscured by modern towns. Importing this data 
into a GIS with satellite data and maps will allow archaeologists the ability to analyze 
multiple levels of data. Archaeologists should recognize the benefits and weaknesses of 

Figure 2.19 Southern California, 2004 striped Landsat image, image courtesy of NASA. 



remotely sensed data as it is used, but should recognize it is through complimentary 
data sources that a more complete record of the past can be built. Having a holistic 
approach makes this possible. 

Aerial photography is the closest thing archaeologists have, at present, to real-time 
imagery, making imagery taken from kites or balloons (Tartarton 2003) helpful during 
excavation. Given the restricted nature of aerial photographs in many areas of the world 
these methods have proven successful in obtaining similar results to air photography 
(Summers 1992; Knisely-Marpole 2001). Archaeologists also have additional control 
over balloons or blimps: One team used a blimp with a remotely controlled 35 mm 
camera with fixed base points for map creation (Summers 1992). Additional control 
may be provided through the use of a helikite, which allows (with no wind) for exact 
height control (Verhoven and Loenders 2006). If the quality of satellite imagery improves 
to a resolution of 10 cm or less, will the field of aerial archaeology merge with satellite 
archaeology? This is a likely outcome. 

A note of the importance of individual scholars working in the field of satellite archae- 
ology also belongs in a chapter on its history. From the beginning, NASA archaeologist 
Tom Sever has been a champion of the use of satellite archaeology, and has a worldwide 
reputation for his generosity regarding information sharing (whether providing satellite 
imagery or advice) and general support, in addition to his comprehensive scholarship. 
Without his encouragement, it is highly unlikely that many archaeologists would have 
even considered using satellites in their work in the early 1980s. Those archaeologists 
went on to encourage the present generation of young scholars writing many of the arti- 
cles on satellite archaeology. Senior scholars in the field of satellite archaeology include 
James Wiseman, Payson Sheets, Elizabeth Stone, Ronald Blom, and Farouk el-Baz, who 
are also known for their willingness to help and support junior remote sensing scholars. 
This spirit of support can perhaps be connected to the general global nature of remote 
sensing, and is a notable feature at conference meetings. It is hoped that future gen- 
erations of remote sensing scholars will carry on this torch: Only by having a global 
perspective on the past that we can begin to shed light on past changes to predict future 




Prior to conducting remote sensing research, an archaeological team must decide what 
imagery to use for their analyses. Where to start is not something previous authors 
have discussed in an in-depth fashion. This chapter will address many issues related 
to choosing appropriate satellite imagery for nearly any research area in the world. 
Before ordering a satellite image, information should be obtained about the cost, date, 
resolution, spectral coverage, spatial coverage, and availability of each imagery type 
under consideration. How will archaeologists know what imagery to consider for each 
landscape type? Teams may not be aware of the specific archaeological features they 
might encounter, or the most appropriate imagery to choose. Covering every type of 
known satellite imagery in this chapter would not help, as there are dozens of satellite 
image types. As with all technology, additional advances will make possible the use of an 
even wider variety of imagery types. For the purposes of this book, it is prudent to focus 
on the most common satellite imagery utilized by archaeologists, shown in Table 3.1. 
Important information will be provided for each imagery type, so archaeologists will 
have a good understanding of the spectral, spatial, and temporal properties of each, as 
well as the advantages or disadvantages for use in satellite archaeology. 

Once archaeologists and remote sensing specialists have identified what imagery is 
most appropriate for their landscape type(s) and potential features, they can begin the 
process of ordering imagery. This chapter will walk potential users through the process 
ordering of satellite images from a variety of online sources, describing the general 
features of satellites one would need for ordering in a specific situation. Some imagery 
is easily obtained, while other types might require additional training or expertise. It is 
important to know how each satellite image type can be applied to archaeological work; 
however, until the imagery is acquired, it cannot be analyzed. This chapter allows for a 
discussion of how to obtain imagery. Viewing and analyzing archaeological features and 
landscape types with satellite imagery will be reviewed in great depth in Chapter 4 and 5 . 

The imagery types and imagery viewing online systems to be reviewed in this 
chapter include Google Earth™, NASA's World Wind, Corona High Resolution Space 
Photography, KH-7 and KH-9 imagery, Landsat, SPOT, ASTER, SRTM, IKONOS, 
Quickbird, SIR-A, SIR-B, SIR-C and X-SAR, as well as imaging systems flown on 
aircraft (LIDAR and SAR). Reference books on remote sensing cover additional types 
of imagery, and the images discussed in this chapter are by no means the only satellite 
images archaeologists may apply in their research. They are, however, the most versatile, 



Table 3. 1 Comparative satellite imagery and multiple imagery viewing programs table (prices 
are current as of January 1, 2009) 








Google Earth™ 


10cm-30 m 


World Wind 


10 cm-30 m 

Visual -pseudocolor 



1-120 m 



US$ 1000-4000 

2-3 m 

0.49-0.59 urn 



15-60 m 

Visible (0.45-0.69 urn) 
IR (0.76-0.90 urn) 
Middle (1.55-1.75 urn) 
Thermal (10.4-12.5 urn) 
Mid-IR (2.08-2.35 urn) 



0.8-20 m 

Visible (0.43-0.47 urn; 
0.50-0.59 urn; 0.61-0.68 urn) 
Near infrared (0.79-0.89 urn) 
Mid-infrared (1.58-1.75 urn) 



15-90 m 

VNIR (0.520-0.600 urn; 
0.630-0.690 urn; 
0.760-0.860 urn; 
1.600-1.700 urn) 
SWIR (2.14-2.225 urn; 
2.360-2.430 urn) 
TIR (8.125-8.825 urn; 
8.925-9.275 urn; 
10/120-11.650 urn) 



0.3-90 m 



US$10-28perkm 2 

0.6-2.4 m 

Panchromatic (0.526-0.929 urn) 


US$7. 70-13.20 km 1-3.2 m 

SIR-A/B/C/X-SAR US$40-50 

15-45 m 

Blue (0.445-0.516 urn) 
Green (0.506-0.595 urn) 
Red (0.632-0.698 urn) 
Near IR (0.757-0.853 urn) 

Panchromatic (0.526-0.929 urn) 
Blue (0.445-0.516 urn) 
Green (0.506-0.595 urn) 
Red (0.632-0.608 urn) 
Near IR (0.757-0.853 urn) 

HH, W, L-Band, X-Band, 

Note: IR = infrared, VNIR = Visible Near Infrared, SWIR = Shortwave Infrared, TIR = Thermal Infrared. 

and largely, cost-effective, imagery types available; many are free, low-cost, or available 
for purchase. 

Although the focus of this book is not specifically on satellite imagery analysis, some 
basic remote sensing terms and concepts are presented for the reader's convenience. 



A discussion of the electromagnetic spectrum and what it represents is also crucial, given 
the overall importance of the electromagnetic spectrum in remote sensing. Satellites orbit 
the Earth collecting radiation reflected from its surface. These are what are known as 
"passive" satellites, collecting reflected radiation. "Active" satellites, such as RADAR, 
send pulses of energy to the Earth's surface, and reconstruct surfaces based on the amount 
of time it takes for the energy to return to the satellite. Passive satellites record data in 
different parts of the electromagnetic (EM) spectrum. Thus, what is the EM spectrum, 
and how is it related to satellite remote sensing? (see Figure 3.1) The EM spectrum is 
essentially a grouping of different types of radiation. It ranges from gamma rays and 
X-rays to radio and TV waves. The range recorded by satellite imagery ranges from the 
visible spectrum (which human beings can see) through the near, middle, far infrared 
(IR) and thermal portions, which humans cannot see. Fast moving particles such as the 
X-rays and gamma rays create higher energy radiation, while radio and TV waves have 
the lower energy radiation. We know that the Sun is a higher source of radiation, and 
falls under the "ultraviolet rays" category in the EM spectrum. EM radiation travels in 
wave-like patterns with different energy amounts. Satellites record this data in what are 
known as "bands." Each band within a satellite image corresponds to a specific range 
of the EM spectrum. Each satellite image has a varying number of bands composed 
of very different types of EM spectrum data, shown in Figure 3.2. "Hyperspectral" 
satellite images record data in multiple parts of the EM spectrum. For example, ASTER 
imagery records data in multiple bands, including the IR and thermal parts of the 
EM spectrum. 

When viewing satellite data using remote sensing programs, the user specifies three 
bands to view simultaneously. By blending the data in varying combinations of bands, it 
is possible to make areas of interest appear more clearly. For instance, the thermal part of 
the EM spectrum shows reflected heat, while the IR part is useful for viewing different 
types of vegetation. A customary way to view satellite imagery is referred to as 3-2- 1 RGB 
(red, green, blue) as shown in Figure 3.3. In other words, the third, second, and first 
bands of that particular satellite image are displayed in the red, green, and blue parts of 
the EM spectrum, respectively. In satellite imagery analysis, the term jam (micron) refers 
to wavelength, and is the measurement used to describe the electromagnetic spectrum 
(Jensen 1996: 40; Lillesand et al. 2004: 5-9). A measurement chart is provided in this 
chapter to show what parts of the EM spectrum each satellite image covers. As each 
satellite image type is described with reference to its archaeological applications, more 
specific remote sensing terms will be defined. 

Google Earth™ 

Advantages: free; available 24 hours a day; global coverage; accessible from Mac or PC; 

easy to use; can upload photos or points; can view 3D landscapes 
Disadvantages: non-global, high-resolution coverage, some areas have 30 m resolution 

coverage; limited 3D coverage of landscapes; difficult to see sites in dense canopy 
Features: can view entire archaeological sites, buried walls, and architecture, can view 

old river courses in desert locations, etc.; users can upload photographs of sites and 

Resolution: .6m— .30m 



Wavelength Spectral 
(p.m) Region 



-10 Ultraviolet 


\ "tsible 



-1 .0 Sear & Mid 





_1 q 4 Microwave 




-10 u 


Figure 3.1 The electromagnetic spectrum, adapted from 
archeology/images/remote_sensing/spectrum.gif, Image courtesy of NASA. 



Figure 3.2 Lake Peten Itza, Guatemala, 4-3-2 RGB Landsat (scale 1: 14400 m), the lighter pixels 
represent vegetation, 1988 image courtesy of NASA. 

Cost: Google Earth, free; Google Earth™ Plus, US$20; Google Earth™ Pro, US$400 
or free 

Discussing Google Earth™ and its potential applications for satellite archaeology is a 
straightforward way to introduce archaeologists to the different types of satellite imagery 
available. Google Earth™ is a commonplace tool in everyday life; many people have 
zoomed in on their homes, universities, or favorite archaeological sites. For future archae- 
ologists, Google Earth™ allows for a Victorian-style "grand-tour" every day. Young 
children, using Google Earth™, can visit Machu Picchu, The Great Wall of China, 
Stonehenge, and The Pyramids of Giza (Figure 3.4), all before bedtime. This will cer- 
tainly have an enormous impact on the future of archaeological practice. University 
students no longer have to rely on black and white aerial photographs to see larger 
landscape views of archaeological sites discussed in class: Lecturers now use images from 
Google Earth™ in most archaeological lectures, bringing landscapes and past peoples to 
life. Such vivid imagery also allows for detailed discussions about the impact the modern 
world is making on past landscapes. Students can see for themselves how the cutting 
down of rainforests in Central America is affecting the illegal antiquities market, or how 
archaeological sites in Iraq have changed in appearance to look like "waffles" due to the 
extensive looting taking place there. 

Google Earth™ is the most well-known public gateway to satellite imagery. Most peo- 
ple see Google Earth™ images on evening news broadcasts. Accessing Google Earth™ 
is straightforward: One goes to the Google Earth™ website, downloads one of three 



Figure 33 Lake Peten Itza, Guatemala, 3-2-1 RGB Landsat (scale 1: 14400 m), visual image, 
1988 image courtesy of NASA. 

versions (Standard, Plus, or Pro), and then zooms to any place in the world. Locations 
are accessed by name or by coordinates. Google Earth™ allows the user to turn on or 
turn off geographic content, roads, borders, labels, extensive global photo-gallery, ter- 
rain, and 3D buildings. Users have the ability to mark particular places of interest and 
polygons can be drawn over features, with varying colors, shapes, and lines. Users also 
may choose to have polygons appear at a certain altitude, or specific exact latitude and 
longitude. Paths can be drawn between one or more features, while users can produce 
their own imagery overlays, uploading their personal images or GIS data. All of these 
features have applications to archaeological work: Teams can upload features or photos 
from a season with a special "team only" user name and password, protecting information 
about locations of archaeological sites from looters. 

Google Earth™ was started by Keyhole, Inc., and was formerly called Earth Viewer 
before Google acquired it in 2004. The name changed to Google Earth™ in 2006. 
Google Earth™ is essentially a virtual 3D globe with imagery and topographic data from 
multiple satellite image types, aerial photographs, and the Shuttle Radar Topography 
Mission (discussed later in this chapter). Google Earth™ can be run on a PC or Mac, 
but cannot currently be run on Linux. Resolution varies on Google Earth™: Some 
cities in the USA have aerial photography with a resolution of .1 m (Figure 3.5) while 
other parts of the world have resolutions ranging from 0.6 to 30 m. Being a publicly 
available resource has resulted in controversy, as both India and Thailand have asked 
Google Earth™ to remove higher resolution imagery revealing their military bases. 



Figure 3.4 Giza Pyramids, Egypt (scale 1:351 m), 2008 image courtesy of Google Earth™ Pro. 

Google Earth™ has refused to comply, underscoring the "democratization" of once 
highly classified information (Hafner and Rai 2005). 

Google Earth™ Pro is required to download the highest resolution imagery, with a 
license fee of US$400, while the Google Earth™ Plus version is available for US$20. 
Using the free version of Google Earth™ provides only grainy imagery when zooming 
in on areas with otherwise higher resolutions. Academic researchers can obtain a free 
version of Google Earth™ Pro by writing to Google Earth™. This is a valuable resource, 
as Google Earth™ Pro allows its users to download TIFF image files at a resolution of 
4800 dpi, compared with 1000 dpi for regular users, and allows users to import their 
GIS data, create movies, and have access to technical help. 

Google Earth™'s applications in archaeology are being explored (Handwerk 2006), 
yet this form of remote sensing technology has disadvantages when used alone. While 
Google Earth™ is free and publicly available, there may be restricted access to this site 
in developing nations. Only some regions of the globe are covered by high-resolution 
satellite imagery and aerial photographs (Figure 3.6). Over large parts of Africa, the 
resolution of the satellite imagery can range from 30 m pixel resolution from Landsat 
imagery to 5—10 m resolution via SPOT satellite imagery. One can never predict the 
imagery quality in any given area, and imagery on Google Earth™ is constantly being 
updated. Most images are current to three years, while many are not stitched together 
correctly. Place names can also be incorrect. Lack of detailing in some mountainous 
regions can make some areas difficult to view, especially the shadow sides of tall features. 

The advantages of using Google Earth™ for archaeological research outweigh the dis- 
advantages, but the disadvantages must be acknowledged. Google Earth™ can provide 



Figure 3.5 Timothy Dwight College (scale 1:58 m), Yale University, New Haven, CT, with 
the arrow pointing to a Ginko tree in the Timothy Dwight courtyard. Note the 
construction visible to the left in Silliman College, 2008 image courtesy of Google 
Earth™ Pro. 

similar information to aerial photographs. The highest resolution satellite imagery in 
Google Earth™, Quickbird, has a standard 0.6 m resolution, while aerial photographs 
have widely varying resolutions, depending on the height of the airplane, type of film 
used, and general flying conditions, but can have a resolution as good as 10 cm. Sites 
that archaeologists know to exist, but do not appear in publications, can be examined 
in detail using Google Earth™. Being able to create wide-format maps for publication 
and in-field use are two additional valuable uses of Google Earth™. Potential applica- 
tions of Google Earth™ and other imagery in the future of landscape archaeology are 
important for assessing ongoing issues relating to looting and urbanization. For exam- 
ple, one important contribution that Google Earth™ provides is determining which 
archaeological sites are being destroyed by urbanization or looting. Through compari- 
son of current Google Earth™ imagery with past aerial photographs, or older satellite 
imagery, archaeologists might be able to see if looting has taken place at the sites. This 
is naturally limited by the single shot of Google Earth, which may not be recent enough 
to capture evidence of looting. Archaeological sites can be understood in their broader 
landscapes, especially in Google Earth™'s 3D mode. 

Are we able to say one is truly "doing" remote sensing when using Google Earth™? 
Although the answer to this question is frequently "yes," in reality this action only 
deals with a small percentage of remote sensing possibilities. For example, most people 
can learn to zoom in on a target on a satellite image to find a mound or monument. 



Figure 3.6 Aerial photograph of Trinity College (scale 1:61 m), Cambridge University, UK, the 
small dots in the courtyard are people, 2008 image courtesy of Google Earth™ Pro. 

However, it is not curently possible to view multispectral imagery on Google Earth™, 
so no detailed remote sensing analysis is possible. Remote sensing offers a far greater 
range of applications. When utilizing satellite imagery, the bias, error rates and methods 
of analysis must be discussed in detail. 

Even though Google Earth™ can be used to locate buried walls or structures using 
higher or lower levels of vegetation, or cropmarks, as seen in Figure 3.7, it is also possible 
to misinterpret what may exist beneath fields. In the fields surrounding the West Bank of 
Luxor, a number of possible crop marks appear that seem to be the paths of tractors. The 
image date is one of the most important features in satellite remote sensing: Seasonality 
influences crop types and weather patterns, which in turn influence how specific features 
such as archaeological sites can be viewed or revealed. In the Egyptian Delta, a majority 
of the Google Earth™ imagery currently online was taken in either late spring or fall, 
in the early part of the growing seasons, owing to the discernable height of the adjacent 
crops. A database for Delta sites thought to be "destroyed" assisted with locating specific 
areas in which to search for cropmarks (see Due to the nature 
of the technology, the ability for archaeologists to rely upon cropmarks to indicate such 
well-defined subterranean features remains "hit and miss," as there are many variables 
which might affect possible cropmarks. 

When Google Earth™ imagery is updated it aids ongoing excavation work. Visiting 
Google Earth™ on two different occasions provided the author with an "in progress" 
view of the 2002 and 2004 excavation seasons at the fortress of Tell Ras Budran, dating to 
ca. 2200 bc, located 160 km south of Suez along the West Coast of the Sinai Peninsula. 



3.7 Cropmarks in the East Delta, Egypt (scale 1:201 m), near the well-known site of 
Tanis, the cropmarks are the lines in the vegetation at a 30-degree angle, 2008 image 
courtesy of Google Earth™ Pro. 

The current image of the site was taken 3.5 years after the 2004 excavation season. 
Although the fort was not discovered via Google Earth™, one can explore the overall 
relationship between the fortress and other potentially undiscovered sites in the region 
(Figures 3.8a and 3.8b). Roughly 4 km to the south, within the compounds of several 
commercial and other installations that bound the coastline, several features appear on 
Google Earth™ with similar shapes to Tell Ras Budran. These circular "structures" 
appear to be roughly half the size of the northern fortress, and lie within a large wadi 
system that could have provided the expeditions with seasonal water. The forts appear 
to be within fire or smoke signaling distance, but lay beyond viewing distance of the 
northern fortress. Without Google Earth™, it would not have been possible to detect 
these sites, owing to their location within restricted areas. However, any remote sensing 
can only aid in selecting specific areas for survey, and cannot replace on-site ground 
assessments. Thus, while the first three circular features appear to be related to Tell 
Ras Budran based on their shape, size and location, it is hoped that permission will be 
granted for surface visits to confirm these assessments. 

What does the future of Google Earth™ hold for archaeology? Many individuals 
have viewed Google Earth™ images of well-known archaeological sites along around 
the world (Beck 2006; Conroy et al. 2008; anonymous NASA report 2005). However, 
all archaeologists should consider the use of Google Earth for site survey in their areas of 
interest for the discovery of potential buried and unknown sites. This is not the work of 
one individual. Most governments have their own cultural heritage management bodies, 



and sharing new discoveries made with Google Earth™ could assist foreign governments 
with preserving and managing previously unknown sites. Archaeologists should keep in 
mind the limitations of Google Earth™, yet also should be aware of all that it can offer 
when used in combination with detailed ground survey work. The overview presented 
here is preliminary in that it summarizes what is and is not possible using Google 
Earth™. Much remains to be learned from publicly available satellite imagery, and 
Google Earth™ is one technological innovation that will continually be improved over 
time. Archaeologists can draw diverse conclusions from crop marks or apparent "sites" 
that appear on Google Earth™, but confirmation is needed through subsequent (and 
additional) remote sensing, ground survey, coring, excavation, and subsurface survey 
using magnetometer, resistivity, or ground penetrating radar. 

NASA World Wind 

Advantages: free; available 24 hours a day; global coverage; accessible from PC, Mac, or 

Linux; easy to use; can upload photos, points, or GIS data; can view 3D landscapes 
Disadvantages: non-global, high-resolution coverage, some areas have 15—30 m 

resolution coverage; do not know exact time or date of imagery 
Features: can view entire archaeological sites; can see buried walls or architecture and 

old river courses in desert locations; can see landscape changes over time, including 

vegetation changes 
Resolution: 1—30 m 

Accessibility: http://World 
Cost: free 

World Wind is an online global imagery viewing program, created and run by NASA, 
with many similarities to Google Earth™. The biggest difference is that the full version 
of World Wind is entirely free. Released in 2004, individuals can view not only the 
Earth, but can also view satellite imagery of the Moon, Mars, Venus, Jupiter, stars and 
galaxy. Viewers can use World Wind on PC, Macintosh and Linux systems. Over five 
million placenames exist in World Wind. Within the USA, aerial photographs have 
provides photographs with resolutions that range from 10 cm— 1 m. Outside the USA, 
resolution can range from 15—30 m. Unlike Google Earth™, there are 13 possible views 
of the Earth, including NASA's "blue earth," composed of MODIS satellite imagery. 
Also unlike Google Earth™, users can compare Landsat imagery from 1990 with Land- 
sat imagery from 2000 to track broad landscape changes. It is also possible to see the 
Earth using pseudocolor, or false-color imagery, and to see the Earth in NASA's scien- 
tific visualization system. This includes seeing satellite imagery that shows scenes of 
agriculture, the atmosphere, the biosphere, climate indicators, the cryosphere, human 
dimensions, oceans, the solid earth, spectral/engineering, and sun— earth interactions. 
These views include events such as the African fires in 2002, an aurora over the North 
Pole and changes before and after the El-Nino event. 

While World Wind is not as user friend as Google Earth, it has more advantages 
from a remote sensing perspective. Google Earth™ is, essentially, a geographic viewing 
program. World Wind can be used for commercial and personal purposes by importing 
data through adding placemarks, paths, lines, polygons, ESRI shapefiles, 3D texture, 
Python or Direct X texts, layers for higher resolution imagery, and additional scripts. 



Figure 3.8 Tell el-Markha Fort, South Sinai, Egypt (scale 1:130 m) with: (a) the 2004 excavation 
visible from space; (b) the so-called southern "forts" (scale 1:81 m) need investigation, 
2008 images courtesy of Google Earth™ Pro. 

In this sense it is not as limited as Google Earth™. Users can also adjust sun shading 
and atmospheric scattering, two important remote sensing adjustments that affect how 
features appear within imagery. The Digital Elevation Model created in World Wind 
is generally more accurate than Google Earth™. Imagery used within World Wind 
include Landsat 4 and Landsat 5 imagery, visible bands of Landsat 7 imagery, and black 
and white and color aerial photos, mainly of the USA, but also of other cities in the 
world. It is possible to download and use images from World Wind, in remote sensing 

Viewing landscape changes through time is something that has become essential in 
archaeology: Only by understanding broad landscape changes can archaeologists make 
plans for preserving and protecting archaeological sites. One does not need intensive 
training to understand World Wind, although a scientific background helps with using 
the full range of tools available for use. By examining longer-term landscape changes 
and false-color imagery, shown in Figures 3. 9a and 3.9b, archaeologists can get a better 
sense of what multispectral imagery can do for archaeology, and will aid in the remote 
sensing learning process. An online guide, "World Wind Central", can aid in importing 
user data, which makes World Wind a far more versatile tool than Google Earth™ for 

Corona High Resolution Space Photography/KH-7/KH-9 

Advantages: preserves views of many vanished landscapes; high resolution; inexpensive; 

fairly straightforward use; global coverage; viewable on any image viewing program 
Disadvantages: imagery can be grainy; image distortion; need negatives for best 

resolution; non-multispectral; need to georeference; sometimes memory intensive 
Features: can view entire archaeological sites, buried walls and architecture, vanished 

landscapes and associated environmental features 
Resolution: .6—150 m 




Airphoto:^al001 /airphoto. html 

Cost: US$30 per scanned negative 

Corona high-resolution satellite photography is imagery that has become quite valuable 
to archaeologists, due to its high resolution, low cost, ease to obtain, and its value in 
recording landscapes now built over or destroyed, shown in Table 3.2. Declassified in 
1995 by an executive order from President Bill Clinton, the US government released 
860,000 images (taken between I960 and 1972) to the public at the cost of reproduction 
only, now available through the United States Geological Service, EROS Data Center, 
near Sioux Falls, South Dakota. Initially, the military represented the body with primary 
access to this technology, with many advances taking place during the Cold War (Mac- 
donald 1995). The declassified intelligence imagery was code-named Corona, Argon, 
and Lanyard, named after the satellites which took the imagery. The Corona satellite 
photographs represent the first global imagery dataset of the Earth, and can aid in filling 
in data gaps between aerial photographs from the 1940s— 1950s and the earliest mul- 
tispectral images from the 1970s. The CORONA systems included the names KH-1, 
KH-2, KH-3, KH-4, KH-4A, and KH-4B, while the ARGON system was designated 
KH-5 and the LANYARD system KH-6. The image resolution of each system varies 
greatly, from 1— 2m for the KH-4B system to 460 feet for the KH-1 system (Fowler 
1996b; Fowler 1997b). The imagery is inexpensive, costing US$30 for scanned negatives 
for negatives (which is generally recommended over prints, for each time the original 
information is copied the quality degrades). This imagery can be ordered through an elec- 
tronic catalogue of the United States Geological Service Global Land Information System 
(GLIS), or through the Earth Observing System Data Gateway (EOS). It is important to 
view the actual image before purchasing, because more than 40 percent of the imagery 
contains cloud cover. Imagery comes digitally in "strips" measuring 2.8 x 29.8 inches 
(Figure 3.10). 

KH-7 and KH-9 space photography represent 50,000 images taken between 1963 and 
1980 by the KH-7 Surveillance System and the KH-9 Mapping System. These images 
were the second set of imagery declassified by the National Imagery and Mapping 
Agency's "Historical Imagery Declassification Conference: 'America's Eyes: What we 
were seeing.' " The KH-7 satellite took images of Soviet and Chinese nuclear installations 
as part of the Cold War and flew from July 1963 to June 1967. KH-7 imagery covers an 
area 22 km wide and between 9 km and 740 km in length with as good as a 0.6— 1.2 m 
pixel resolution. The KH-9 satellite mission, flown from 1973 to 1980, was dedicated 
to mapping. Each KH-9 image comes in either black and white or color satellite photos, 
covers an area of 130 x 260 km at a scale of 1:500,000 and has a pixel resolution of 
8-10 m (Fowler 2004). The world is generally well-covered by the KH-7 and KH-9 
missions (black and white), while KH-9 color images only cover select parts of the globe. 
When used as part of the author's research in Egypt's Delta regions, it was found that 
the KH-7 imagery did not have as high a resolution as the scanned KH-4B Corona 

Corona imagery is one of the most widely-used datasets for archaeological research 
(Fowler 1997a, 1997b; Mathys 1997; Fowler and Fowler 2005), especially in the Middle 
East. This is due largely to the 1111 mission of the KH-4B system, one of the primary 
missions mapping the cease-fire between Egypt and Israel. In some cases, the ground 



















































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Figure 3.9 Landsat images of Alabama in: (a) 1990 (scale 1:25792 m); and (b) 2000 
(scale 1:25792 m), the growth is represented by the slightly lighter pixel colors, 
images courtesy of NASA World Wind. 

resolution of this mission can reach 1 m. Corona imagery has helped to reveal locations 
of "hollow ways," or tracks between settlements of Syria (Ur 2003), and has assisted 
with confirming the location of previously unknown settlement sites in Egypt (Parcak 
2007a, 2007b). Population increases and urbanization have greatly altered landscapes 
over the past 40 years, and Corona imagery can help detect features long-since buried, 
destroyed, or altered by modern farming techniques (Donoghue et al. 2002). Sites now 



gone due to dam construction or additional building can be mapped, and in same 
cases, relocated, using Corona imagery (Donoghue et al. 1998; Kennedy 2002). Corona 
images also allowed an intensive landscape assessment to be made in South Siberia's 
Altai Mountains. A cold climate had preserved many of the burial mounds built by 
nomads. Archaeologists studied the relationship of burial mounds to cemeteries in a 
survey of more than 3000 sites (Goossens et al. 2006). 

Corona imagery has a number of disadvantages compared to other forms of satellite 
imagery. It is essentially high resolution black and white space photography, and does 
not contain any color or multispectral data. Imagery from the Corona satellites cannot 
be used alone to create maps due to significant s-shaped distortion: Different resolutions 
appear on different parts of the imagery, with a smaller scale appearing the further one 
gets from the main axis of the imagery. Issues exist with the pitch and yaw (or move- 
ment) of the satellites. Stereoscopy, or viewing imagery in 3D, is possible with forward 
and backwards-looking imagery taken from the same locations, but imagery displace- 
ment and distortion can cause issues with height differences. Additionally, both images, 
though taken from the same point, can have different viewing angles and contrast. Good 
ground control points are needed to correct these problems, which can be an issue with 
landscape changes over time. Multiple imagery datasets can also correct parallax, or dis- 
placement of an image when viewed from multiple locations. Different GPS tools can 
be used to help correct the view depending on the level of accuracy needed for ground 
control points (Goossens et al. 2006). 

Corona imagery's many advantages make them attractive to archaeologists 
everywhere. Prior to 2005, archaeologists needed to scan their own Corona imagery. 
Now, the USGS website sends digitally scanned negatives at 23, 14 or 7 jam. Although 
this saves time, archaeologists have determined that the best results occur from Corona 
imagery when it is scanned at a minimum of 5 jam. Experimentation by the author 
comparing 10 jam and 5 jam scanned Corona imagery produced noticeable results. At 
5 um, the imagery's details proved to be sharper, with small lines defined more clearly, 
shown in Figure 3.11a when compared to Figure 3.11b. In order to georeference the 
Corona data, either imagery maps of study sites or unchanged ground control points 
are needed for plotting points. The latter can be difficult to obtain, especially in areas 
not yet surveyed. Google Earth™ can help in determining if an area is unchanged if 
maps are not available, but only in cases where higher resolution data exists. Computer 
programs such as Arc View or Airphoto allow for georeferencing. Airphoto, available 
for a small cost on the "Bonn Archaeology Software Package" homepage, is a program 
developed by renowned aerial archaeology specialist Irwin Scollar. The program was 
developed especially for any types of aerial imagery with significant amounts of dis- 
tortion, and requires a small amount of user input other than selected ground control 
points. Airphoto also creates Digital Elevation Models (DEMS), or 3D digital landscape 

Imagery quality will vary, but the KH-4A, KH-4B and KH-6 systems have the 
best resolution and quality for archaeological work. Earlier images may be useful for 
documenting site change. The KH-4B system solved problems, including satellite 
vibrations and static electricity, and had a far better film quality than previous sys- 
tems with 160 line pairs per nanometer. Even though the 1970 KH-4B mission has 
an approximate 1 m resolution, atmospheric conditions affected additional imagery 
(Donoghue et al. 2002). Corona imagery can provide forward and back views on the 



Figure 3.10 Photograph of Corona strip, image by Sarah Parcak. 

same areas, and can be used as orthophotos to create basemaps for archaeological projects 
(Goossens et al. 2005; Kennedy 1998). While cropmarks may be difficult to view 
on Corona imagery, the imagery can detect buried features on archaeological sites. 
Corona imagery of Tell Tebilla, a ca. 600 bc site in the eastern Egyptian Delta, led 
to the discovery and subsequent excavation of a temple enclosure wall (Mumford 2002, 

Studying a region in depth in Corona imagery prior to georeferencing can give a "feel" 
for the landscape. In some instances, a landscape will be little altered in over 40 years, thus 
facilitating archaeological analysis. These cases are rare: Most archaeologists comment 
on how landscapes have changed over a 40-year period. Viewing Corona imagery is 
similar to viewing black and white aerial photography. Choosing easily recognizable 
ground control points such as large rocky outcroppings, unchanged buildings, or other 
significant landmarks aid in the georeferencing process. Once georeferencing is complete, 
archaeologists should spend time describing a known archaeological site, which can assist 
in picking out features not noticed previously. Perhaps the site changed little in 40 years, 
or perhaps farming has destroyed a large portion of it. Features such as hollow ways may 
not be apparent in modern-day imagery. Noting any unusual features in the landscape 
or on top of archaeological sites will assist with additional imagery analysis. Importing 
Corona imagery into a GIS allows comparison with multispectral remote sensing data, 
as turning the Corona layer on or off may show archaeologists how much landscapes 
have changed. 




Advantages: preserves high resolution views of vanished landscapes; fairly 

straightforward use 
Disadvantages: expensive; some image distortion; non-multispectral, non-global coverage 
Features: can view entire archaeological sites, buried walls or architecture, and large 

parts of sites now removed due to urbanization over the past 20 years 
Resolution: 2—3 m 

Cost: US$1 000-$4000 

KVR-1000 imagery, from Russian COSMOS satellites released in 1987 (Fowler 1995a; 
Donoghue and Galiastagos 2002), is high-resolution black and white space photography, 
with a resolution of 2 m. Resolution ranges between 2—4 m, with each image covering 
an area of 40 x 40 km or 40 x 160 km at a scale of 1:220,000. Images come in scanned 
strips measuring 18x18 cm. Through private companies, each user can request imaging 
for specific areas if they are not available online. Currently, all parts of the world are 
available for order except Russia. Images come as either "raw" (meaning there are no 
geometric or radiometric corrections) or "orthorectified", meaning that these corrections 
have been applied with a +/— 20 m accuracy without ground control points. The 
high costs of KVR-1000 imagery make it prohibitively expensive for archaeologists, 
compared with Quickbird high resolution satellite imagery; typically costs range from 
US$1000 for 16 km, US$2500 for 1000 km; US$1600 for 160 km; and US$4000 for 
1600 km. Spectral data is restricted to the visual part of the spectrum, from 510—760 
nm. Archaeologists have not used KVR-1000 photography as widely as other types of 
satellite imagery. The first tested use of KVR-1000 detected Stonehenge from space 
(Fowler and Curtis 1995). It has also helped to detect previously unknown Roman roads 
and Turkish walls (Comfort 1997a, 1997b, 1998, 1999). Until the cost of KVR-1000 
imagery comes down, it is unlikely it will play a major role in future archaeological 
remote sensing research. 


Advantages: global coverage from 1972— present; multispectral; can analyze a wide range 

of landscape types 
Disadvantages: non-high-resolution banding on imagery from 2003— present; requires 

knowledge of remote sensing analysis; need remote sensing programs for 

multispectral use 
Features: mutispectral data highlights vegetation, soil and geological features associated 

with past remains; shows how remains can be viewed in seven bands of the EM 

Resolution: 15— 80 m 
Accessibility: (click on "search for imagery" to access free data) 
Cost: free-US$600 

Landsat imagery, first recorded in 1972, has had the broadest usage in archaeology of all 
the types of satellite imagery. This is due to its low cost, worldwide coverage, and the 



numerous techniques one can apply with it. Landsat imagery of a majority of the globe 
can be freely obtained on several websites (listed above). With resolutions ranging from 
15 — 80 m, Landsat imagery's versatility makes it useful to archaeological projects with 
budgets that make higher-resolution data such as IKONOS and Quickbird prohibitive. 
Landsat imagery is most versatile in diverse landscape conditions because of varying 
band lengths in the electromagnetic spectrum (Figure 3.12). The band lengths can be 
combined in various ways in remote sensing programs, or can have algorithms or com- 
puter programs applied to group together like pixel types (Table 3.3). Each band of 
Landsat data is useful for viewing different aspects of the spectrum, and can be com- 
bined in three-band patterns to make distinct certain parts of the landscape. The Landsat 
satellite image band combination most used by archaeologists in their analytical work is 
a 4-3-2 RGB (IR, red, green). These bands (2,3, and 4) are useful for investigating the 
reflectance (green) of healthy vegetation, soil-boundary, and geological boundary anal- 
yses, as well as vegetation discrimination and vegetation biomass, respectively (Jensen 
1996: 40; Lillesand et al. 2004: 5—9). Vegetation detection from space is closely con- 
nected to archaeological discovery: Different types of vegetation, compared to "typical" 
vegetation types for the region, can group on top of or next to archaeological sites. 
Archaeological features are also affected by water sources, making band 5 another useful 
band for archaeological remote sensing analysis. 

The "orthophoto" section of provides archaeologists Landsat 
imagery from the 1970s, 1980s, 1990s, and 2000s. Some imagery on the commercial site costs US$50, which is discounted from the standard price 
of either US$450 or US$600. Landsat images cover a 185 x 185 km area and are sun 
synchronous, meaning that they follow the path of the sun on a 1 6-day cycle around the 
Earth. Landsat imagery appears in a path and row format: Satellites follow a consistent 
orbit around the earth, recording imagery data over set paths and rows. The path and 
row numbers of a Landsat image are available on the Landsat website by clicking on any 
part of the globe. To download free imagery, one needs to choose the year of interest and 
then the path folder of interest. In each path folder, additional folders with row numbers 
are visible. After opening the appropriate row folder, the imagery is downloaded in a 

Figure 3.11 Corona image of Tell Tebilla, Egypt (scale 1:585 m): (a) 1972 image courtesy of US 
Geological Service; and (b) Landsat image of Tell Tebilla (scale 1:1600 m), note the 
lack of detail in the Landsat image, image courtesy of NASA World Wind. 



Table 3.3 Chart of Landsat band values, adapted from 

Band Spectrum type 

Part of spectrum {ptm) Analytical capabilities 

1 Visible (blue) 0.45-0.52 

2 Visible (green) 0.52-0.60 

3 Visible (red) 0.63-0.69 

4 Infrared 0.76-0.90 

5 Middle infrared 1.55-1.75 

6 Thermal infrared 10.4-12.5 



Soil, land-use and vegetation characteristics 

Reflectance (green) of healthy vegetation 

Soil-boundary and geological boundary 
analysis, as well as vegetation discrimination 

Vegetation biomass 

Can assess the amount of water in plants 
and assists in hydraulic research 

Geothermal activity and vegetation 

Geological rock formations 

zipped format. Typically, each band is downloaded separately into the same folder, after 
which the bands are combined into a multilayered image using one of a number of 
remote sensing software packages. 

How can users chose the correct version of Landsat imagery for their archaeological 
work? Landsat 1 imagery, with a spatial resolution of 80 m, is generally too coarse 
to view anything but the largest monumental archaeological features, but can aid in 
examining longer-term environmental trends. Landsat 2 and 3 satellites, with 80 and 
30 m pixel resolutions, respectively, followed in 1975 and 1978. With the 1982 and 
1984 launch of the Landsat 4 and 5 satellites, which contained 30 and 120 m pixel 
(with the 120m resolution) in the thermal band (or the band most useful for detecting 
surface temperatures), researchers now have access to seven bands of information from 
the electromagnetic spectrum. With the earlier Landsat satellites, researchers could only 
utilize five bands of information. The Landsat 4 and 5 bands match the banding of Landsat 
7 data, save the thermal pixel size. Landsat 6 failed in its launch, but the successfully 
launched Landsat 7 ETM+ ("Enhanced Thematic Mapper," with a 30 m pixel resolution 
for every band except thermal, which has a 60 m pixel resolution) provides the most 
current images of the earth of the Landsat satellite group. Landsat 7 ETM+ imagery 
also has 15 m panchromatic imagery. Through various computer programs, the user can 
merge the 15m data with the multispectral 30 m data to create 15m multispectral data. 
In 2003, a technical malfunction caused the Landsat 7 ETM+ imagery to have a series 
of horizontal stripes. This is corrected through destriping the imagery with sections of 
earlier imagery from the same month. Most archaeological projects use both the Landsat 
4 or 5 imagery and the Landsat 7 ETM+ imagery. 

Over the past 30 years, archaeologists around the world have conducted many studies 
using Landsat satellite images (Stargardt 2004). With free imagery, cost is not necessarily 
a prohibitive factor, although the area covered by the free imagery is sometimes limited. 
Features 1 5 m or greater in size appear quite well on Landsat 7 ETM+ imagery, although 
features smaller than a few meters in size can appear in Landsat images if they contrast 
sharply with their surroundings (Lillesand et al. 2004: 377—414). For example, roads 



Figure 3.12 Mt. Desert Island, Maine, Landsat image (scale 1:2245 m), draped over SRTM data, 
image courtesy of NASA World Wind. 

or paths only 5 m wide in the desert show up on Landsat imagery due to major color 
differences in the pixels (Figure 3.13). 

Landsat imagery has a number of advantages and disadvantages for archaeological 
research, with the main advantage being its multispectral data. The various analytical 
methods employed to analyze such multispectral data will be discussed in greater detail 
in Chapter 4, while this section will deal with the broad archaeological questions one 
can approach using Landsat data. With a broad range of multispectral data, Landsat 
satellites can detect not only archaeological sites and features, but environmental factors 
associated with past sites. The first archaeological study using Landsat imagery helped 
to map Angkor Wat through mainly visual site detection (Limp 1992a). By using one 
of Landsat 's bands in an image viewing program (whether through Irfanview, Adobe® 
Photoshop®, or ArcGIS®), general visual assessments can be made of an archaeological 
area of interest by individuals without remote sensing training. One does not gener- 
ally have to identify the exact location of an archaeological site with Landsat imagery. 
Without satellite imagery, where would one begin to look for features of interest (see 
Chapter 7)? Coarser resolution imagery can help archaeologists to zoom in on potential 
environmental indicators. One study used Landsat imagery to locate potential peat bogs 
in the UK to suggest areas for archaeological reconnaissance (Cox 1992). 

An overview of the methodologies in studies using Landsat imagery has implications 
for archaeological remote sensing, especially considering the diverse types of techniques 
applied in studies across the globe. One archaeologist used a Landsat 5 image in the south- 
east region of Mallorca, Spain, to investigate Roman field division systems. Multispectral 



3.13 Sahara Desert (scale 1:7712 m), note that the road running E-W is visible from 
space on 30 m Landsat data, even though it is only 5 m wide, image courtesy of 
NASA World Wind. 

analysis did not play a significant role in identifying the ancient field systems; instead, 
the study looked at rural patterns by correcting the images' geographical points and 
applying basic image enhancement techniques. Although ground-truthing did not play 
a role, the study observed suggestive patterns (Montufo 1997). Use of Landsat 's infrared 
bands helped to detect various architectural features surrounding Stonehenge (Fowler 
1994a, 1995a), although this can be hard to view, as seen in Figure 3.14. The IR bands 
have assisted in identifying many features on sites invisible through the use of conven- 
tional aerial photography. In some cases Landsat analyses are combined with airborne 
remote sensing data to great success. One study used airborne remote sensing data and 
Landsat data to identify sites for future investigation in Scotland (Winterbottom and 
Dawson 2003) 

Not all uses of Landsat images are intended to detect specific archaeological sites; 
past environmental indicators can be just as valuable. Along the west coast of Sinai, 
Landsat satellite imagery detected water sources connected with past mining expe- 
dition campsites dating to between ca. 2500—1800 bc (Mumford and Parcak 2002, 
2003). In northeast Thailand, Landsat imagery's IR bands detected an area covered with 
ancient settlements and canals, archaeological mounds, and smaller dams. There was 
an 80 percent success rate reported for detecting known mounds, with implications 
for detecting previously unknown ancient sites (Parry 1992; Palmer 1993). Changes 
in water sources also affect how populations move and change. Landsat imagery has 



Figure 3.14 Stonehenge, UK (scale 1:142 m), located just below the cross-hatch mark, image 
courtesy of NASA World Wind. 

allowed a study on changing river courses and their affects on human groups in the 
Mississippi region (Showater 1993; Parssinen et al. 1996), as well as Andean lowland 
and the migration of Indians caused by sudden river relocation. Landsat data aided in 
discovering faults near the site of Sagalassens, southwest Turkey, which appeared to have 
caused the abandonment of the site in the mid-7th century ad (Sintubin et al. 2003). 

Archaeologists have enumerated that Landsat imagery resolution is too coarse to 
allow intersite or intrasite remote sensing (Wynn 1990). Although Landsat imagery 
cannot detect small structures, it can detect walls thinner than 15 m or stone features if 
they contrast spectrally with the structures surrounding them. For example, at the site of 
Mendes in the northeast Egyptian Delta, one can see the granite naos (a large, ceremonial 
stone structure built to house images of deities), which measures 3 x 3.5m, in the central 
part of the site, shown in Figures 3.15a and 3.15 b. The granite stone of the naos contrasts 
spectrally with the limestone platform beneath it (Soghor 1967). Archaeologists should 
evaluate Landsat s abilities on a case-by-base basis. Vegetation, in particular in rainforest 
areas, can obscure archaeological features beneath the forest canopy, causing temporal 
issues to become important in imagery acquisition. Certain features may or may not 
appear depending on the time of year; therefore, archaeologists need to obtain Landsat 
imagery accordingly. Landsat imagery is best used for grouping together similar pixel 
groups through imagery analysis, or for running additional algorithms to clarify spectral 
data. At the very least, Landsat images can be used to create basemaps and to assess broad 
landscape changes over time. 



Figure 3.15 Mendes, Egypt, comparing: (a) a Quickbird image (scale 1 :548 m); with (b) a Landsat 
image (scale 1:981 m) note that the arrows point to the granite naos in each image, 
2008 image courtesy of Google Earth™ Pro and Landsat image courtesy of NASA 
World Wind. 




Advantages: global coverage from 1978— present; multispectral; can analyze wide range 

of landscape types 
Disadvantages: requires knowledge of remote sensing analysis; need remote sensing 

programs for multispectral use 
Features: suitable for detecting vegetation changes associated with archaeological sites; 

panchromatic data can detect smaller architectural features 
Resolution: 0.8 m (panchromatic), 5—20 m (multispectral) 
Accessibility:; http://www. 

americavie w. org/ 
Cost: US$1200 (normal scene, 5 m panchromatic, 20 x 20 km, or l/8th scene); 

US$11,750 (orthorectified 2.5 m color merge, 60 x 60 km, full scene), see pricing 

list on the website above; 35—85 percent discount for academic researchers through 

the America View program. 

SPOT, or System Pour L'Observation de Terre, launched in 1978 by the French gov- 
ernment, is utilized in all areas of scientific research, and is especially well suited for 
mapping and producing digital elevation models through stereo pairs. SPOT 1, 2, 3, 
and 4 launched in 1986, 1990, 1993, and 1998, respectively, have a 10-20 m pixel res- 
olution (Lillesand et al. 2004: 439-53). SPOT 5, active since 2001, has multiple pixel 
resolutions in both color and black and white, ranging from 0.8 m to 20 m. SPOT 5 
contains five bands, comprised of the visible (RGB), near IR and mid-IR bands, shown 
in Table 3.4. The two bands in the IR make SPOT a useful satellite image for archae- 
ological investigations that involve vegetation (Guy 1993; Hijazi and Qudah 1997), 
shown in Figure 3.16. One can order imagery from SPOT's website through a global 
server. SPOT provides educational discounts, but the user must request this service by 
completing additional forms. Imagery costs depend on the size of the overall scene, the 
type of scene ordered, image resolution, and additional services. Generally, the cost for 
SPOT imagery ranges in thousands of dollars. 

It is necessary to preview each image before purchase, due to possible cloud cover. 
SPOT images can be ordered at a number of levels, depending on the needs of the user. 
Level 1A is not geometrically corrected (i.e. corrected to a known geometric correction 
with removal of geographic distortion) while Level IB contains corrections but only for 
systemic effects. Unlike Level IB, Level 2 A has geometric corrections matching standard 
map projections such as UTM WGS 84. Level 2B uses known ground control points, with 
a locational error of less than 30 m. Level 3 (ortho) uses a digital elevation model (DEM) 

Table 3.4 SPOT-4 values, adapted from http://www. 

Band Spectrum type Part of spectrum (fim) 


Visible (blue) 



Visible (green) 



Visible (red) 



Near infrared 



Mid Infrared 






■■ -.;*;--,? . 




* - 


itj ;". 



i#fe^; : 




t . 

i^9v 43 




• xl + V « 






* 'J. 

Figure 3.16 East Delta, Egypt SPOT image (scale 1: 14100 m), 2002 image courtesy of SPOT. 

to correct errors, with a location error of less than 15m per pixel ( 
Future satellites are being developed with radar and sub-meter capabilities. 

A number of archaeological projects have applied SPOT imagery in their work 
(Aldenderfer 2003). Visually, SPOT imagery was tested for archaeological research, 
and located a variety of archaeological landscapes in Danebury, UK (Fowler 1993, 
1994d). With better resolution than Landsat imagery, and concentrated bands in the 
visible and IR, the imagery was valuable for detecting archaeological features through a 
technique called classification (grouping similar pixels within an image). One study 
classified data in order to identify faults and ancient road networks in the Arroux 
river valley in Burgundy, France. A SPOT image aided the team in mapping their 
results, which were then fieldwalked (Madry and Crumley 1990). One team did not 
think that remote sensing would be of use due to dense vegetation cover in Greece. 
The SPOT imagery, in fact, was useful in classifying landscapes (Wiseman and Zachos 
2003). The classification of landscapes also assisted with archaeological site discov- 
ery in the Arkansas River Valley (Farley et al. 1990). SPOT imagery, along with 
Landsat and SAR data, helped archaeologists analyze alluvial deposits from Yellow 
River plain in China (Blom et al. 2000). SPOT data also aided archaeologists in 
examining long-term landscape changes relating to settlement patterns in Madagascar 
(Clarke/*/. 1998). 

SPOT has similar advantages and disadvantages as Landsat imagery. The coarser 
resolution imagery makes it difficult to identify smaller archaeological features. Users, 
however, can merge the multispectral data with the 0.8 m resolution panchromatic 
(single band) data, to get 0.8 m IR data. A major disadvantage of this approach is 



the overall cost of SPOT imagery. Images start between US$1500— 2000, with some 
imagery costing as much as US$4500 per scene. Through the "AmericaView" program 
(see website above), academic researchers can pay a US$2500 fee, which is credited 
towards any imagery. With sufficient subscribers to this program, researchers can receive 
up to an 85 percent discount on SPOT imagery, which makes SPOT imagery much more 
affordable to archaeological researchers. 


Advantages: most of the globe is covered; hyperspectral; can analyze a wide range of 

landscape types 
Disadvantages: requires knowledge of remote sensing analysis; need remote sensing 

programs for multispectral interpretation 
Features: hyperspectral data allows more detailed mutispectral analysis 
Resolution: 15-90 m 
Cost: US$80 per scene, free if a NASA partner 

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), costs 
US$80 per scene, including free digital elevation models, with scanners in the visible, 
near-mid and thermal IR portions of the electromagnetic spectrum. It is known as 
hyperspectral imagery, which refers to the multiple bands (15) that compose ASTER, 
seen in Table 3.5. Each ASTER image represents a 60 x 60 km area. Three subsystems 
make up ASTER: (1) the visible and near IR (VNIR), with a 15 m resolution and three 
spectral bands, seen in Figure 3.16; (2) the short wave IR (SWIR), with a 30 m pixel 
resolution and six spectral bands; and (3) the thermal IR (TIR), with a 90 m pixel 
resolution and five spectral bands. This system is particularly useful for digital elevation 
models, which the original data can be draped over to create 3D imagery. This aids in 

Table 3.5 ASTER spectral values, adapted from 

Band type 


Spectral range (/im) 

Resolution (m) 



























































^BTff^ JM f J^_ ■Br* - ±\, 

uL: '^fipBKfe 

*'v?Sv Kir 

; « '^^d^F ■ ' 


'ft. ' .. ^ri. 

4* * ' 

1, i 


Figure 3.17 Lake Peten Itza, Guatemala, 3-2-1 ASTER image (scale 1:6400 m), 2001 image 
courtesy of NASA. 

studying regional vegetation and geology. Like in all remote sensing datasets, the users 
can view only three bands of ASTER data at once, seen in Figure 3. 17. It is recommended 
that the user stay within the same resolution groupings when applying algorithms for 
ease of analysis. 

Fewer case studies exist for ASTER use in archaeology. This is surprising given that 
ASTER is fairly inexpensive and has much to offer for multispectral analysis. In fact, if 
an institution or individual has a connection with NASA or a funded NASA-partner, it 
is possible to download ASTER imagery for free. The ASTER imagery website is set up 
in a global search mode. The user can specify path and row numbers (similar to Landsat), 
or can zoom in on a particular part of the globe. Imagery appears in tiles: Some areas 
have multiple images from different times (months and years), while others do not have 
any imagery. Many ASTER images have cloud cover, especially in regions of the world 
with denser ground cover or in rainforest areas. This cloud cover can be the biggest 
obstacle to using ASTER imagery. Even if an image has cloud cover, it may contain a 
small section useful to the researcher. 

ASTER's hyperspectral data, coupled with 15 m resolution, allow for the detection 
of broader archaeological features, especially using the thermal band of the spectrum 
with features larger than 90 m in size. One study modeled past lakeshores using ASTER 
imagery to create digital elevation models (DEMs) in Egypt's Western Desert (Bubenzer 
and Bolten 2003). The TIR ASTER imagery is too coarse to detect smaller archaeological 



Figure 3.18 Lake Peten Itza, entire ASTER image, image measures 60 x 60 km, note the small 
percentage of cloud cover in the center upper portion of the image, 2001 image 
courtesy of NASA. 

sites, while the VNIR imagery is useful for detecting different vegetation types but is not 
for differentiating between different soil types (Lillesand et al. 2004: 481—3). Looking at 
ground surface vegetation may help detect buried features that affect growth patterns. 
It is likely that sites would be detectable beneath the soil with the application of the 
SWIR ASTER composite image. This band range can differentiate soil types when 
there are large enough openings in the vegetation, and can detect denser concentrations 
of vegetation where moisture content might be greater. This assisted in site detection 
in the Agredos Valley, Italy, where a combination of ASTER and IKONOS imagery 
imported into a GIS was used to find Roman boundaries of old fields (Marcolongo et al. 
2006). Using combinations of different bands of ASTER and algorithms also detected 
multi-period settlement sites in Iraq (Altaweel 2005). 

ASTER imagery's applications to archaeological site and feature detection have not 
yet been fully explored by archaeologists. Use of low cost, medium resolution, visible 
and IR data, combined with coarser resolution thermal data, may lead to discover- 
ies of additional features buried beneath modern landscapes. How archaeological sites 
affect the vegetation covering them is an area not fully catalogued by archaeologists 
using satellite remote sensing. Past cultures built varying walls, temples, tombs, and 
other structures out of numerous materials. These materials all decomposed (and are 
decomposing) in different ways, with chemicals leeching into the soil and absorbed 



by local vegetation. Some materials may cause the plants to grow more leaves, absorb 
additional moisture, or reflect absorbed light in a slightly different part of the spec- 
trum, thus appearing as a different color on multispectral satellite imagery. The soil 
itself may appear differently with chemicals or moisture (Figure 3.18). Hyperspectral 
satellites such as ASTER and the recently released EO-I Hyperion satellite (with 220 
bands in the electromagnetic spectrum ranging from 0.4—2.5 jam with a 30 m spatial 
resolution, see are the best tools 
archaeologists have to detect these subtle changes. 


Advantages: free; available 24 hours a day; global coverage; accessible from Mac or PC; 

easy to use; can download in multiple formats 
Disadvantages: limited, high-resolution coverage; some areas have 30 m resolution 

coverage, some imagery is more detailed than others 
Features: can view landscapes in 3D; can drape other satellite imagery on top of SRTM 
Resolution: 1—90 m 
Accessibility:; http://edc.usgs. 

gov/produc ts/elevat ion . html ; ht tp : //seamless . usgs . gov/ ; http : //glcf . umiacs . umd . edu/ 

Cost: free 

The SRTM (Shuttle Radar Topography Mission) provides 3D global elevation data 
without charge to any user. Flown on the Space Shuttle Endeavor during an 11 -day 
mission in February of 2000, NASA collected the data through a specialized radar 
system onboard the shuttle. The mission represented a joint project between NASA, the 
National Geospatial-Intelligence Agency, and the German and Italian Space Agencies. 
The mission was flown as part of the X-SAR/SIR-C mission. There is global 30 and 
90 m pixel resolution elevation data, with 1—10 m elevation data for the USA. SRTM 
data is employed in many archaeological projects due to its cost and applications to a 
wide range of archaeological questions. Archaeologists can import the data into either 
a remote sensing program or a GIS, and then layer additional satellite data on top of it 
(Figure 3.19). 

Having a good understanding of the Earth's topography is important to the geo- 
sciences, especially geology and hydrology, and is an essential part of cartography. Good 
digital elevation models (DEMs) also have numerous military and civilian applications, 
in addition to archaeological applications, as they all rely greatly on landscape recon- 
struction and visualization. Prior to the launch of the SRTM system, no consistent, 
global topographic map existed. Taking aerial photographs of the Earth did not prove 
feasible due to issues of cost, time, and country-specific military regulations. Previous 
aerial images of the world varied in quality and resolution, if such imagery existed at 
all, so could not be used to create a global database. Cloud cover provided another major 
problem, thus necessitating a scientific technique that would image the Earth's surface 
in 3D. Data for the SRTM was collected through the Shuttle Interferometric SAR, or 
InSAR, acquired by dual antennas with a small base-to-height ratio. As the radar signals 
are returned to the satellite, the measurements are recorded according to a consistent 
reference coordinate system. Where there is dense surface vegetation, the SRTM data 



Figure 3.19 SRTM image of Lima, Peru, (scale 1:244053 m) processed to reflect height 
differences, image courtesy of NASA. 

will reflect its surface and not the ground cover. Rough water, rocky ground, and scat- 
tered areas of vegetation are the ground types best recorded with the SRTM data; flat 
sand or still water can be incorrectly recorded. 

SRTM data offers archaeologists many advantages. Ordering high resolution 
Quickbird data and draping it on top of the SRTM data allows for a near-complete 
landscape reconstruction. One can navigate through a landscape, comparing discovered 
features and sites to natural resources and terrain. Elevation may be an important factor 
for site or feature detection, and people using satellite imagery may note correlations 
between discovered features and their elevations. Global data now exists through a mea- 
surement of 1 arc second by 1 arc second (30 x 30 m), with a relative height position 
error of 6 m, and a ground position error of 20 m. This error is within mapping standards, 
and allows for good correlation with additional satellite datasets. 

SRTM data is available through a number of websites. On the USGS SRTM website, 
it is advisable that all potential users complete the tutorial, as the website can be difficult 
to navigate. Some basic points to follow include checking the box for "STRM finished 
arc 1 sec shaded relief" and "SRTM finished 3 arc sec shaded relief" under "elevation" 
(on the right-hand menu). This will allow the search engine to look for the SRTM 10 
and 30 m data. Once the user has selected an area, the next step is to check "SRTM 
3 arc sec," or whatever other dataset they wish to download. The user can specify in 
which format to download the SRTM data: It is recommended to choose "GeoTIFF" for 
ease of use in remote sensing and GIS programs. Data size will range from 2—15 MB. 
With more terrain detail chosen for display within a remote sensing program, this may 
exponentially increase the size of the imagery, especially if the satellite dataset on which 
the SRTM data is draped is of a large size (such as Quickbird imagery). 

Archaeological teams have used SRTM data to model past landscapes (Golchale and 
Bapat 2006; Evans and Farr 2007), especially in past paleo-hydrological studies (Timor 
2004). Some scholars have suggested using SRTM data to locate or scan for multi- 
period archaeological sites in the Middle East, showing small mounds appearing on 



the SRTM imagery to be past occupation sites (Sherratt 2004; Menze et al. 2006). 
This is problematic in that many of these sites, called "tells," have modern occupations 
overlying them. One cannot show from SRTM data whether a site that is raised is 
free from modern occupation or if it is a raised area with no archaeological material. 
Combined with multispectral imagery, this would be a powerful tool to locate such 
tell sites (Parcak 2007b). Any past landscape reconstruction studies would benefit from 
SRTM data, considering the cost of the imagery and the terrain detail it can provide. 

High resolution imagery: Quickbird and IKONOS 

Advantages: global coverage; multispectral 

Disadvantages: high cost; need remote sensing programs for multispectral use 

Features: both images can detect buried walls, archaeological sites, and aid in 

detailed mapmaking; locate vegetation associated with archaeological sites and 

Resolution: 0.6-3.2 m (Quickbird 0.6-2.4 m and 0.82-3.2 m IKONOS) 
Accessibility:;; http:// 

www. geoey e . com/CorpSi te/ 
Cost: Quickbird costs US$10— 28 per km with additional costs if imagery is express 

ordered; IKONOS costs US$7.70 per km 2 , or orthorectified at US$13.20 per km 2 

Launched in 2001 by Digital Globe, Quickbird satellite imagery is the highest resolution 
multispectral data available for commercial use in archaeological research. Quickbird 
imagery can be ordered in panchromatic (black and white), multispectral, natural color, 
color IR, or in 4-band pan sharpened. With collected points for georeferencing, Quick- 
bird imagery can be accurate to within 2.6 m at a scale of 1:5000 (if Digital Globe 
georeferences the data), or to within 15.4 m at a scale of 1:50,000. This makes it the 
highest resolution data for cartographic usage. Quickbird records data in strips mea- 
suring 16.5 x 16.5 km, or 16.5 x 165 km. There can be 2048 levels of grey scale 
(with 11 -bit digitization), and non-overlapping spectral bands, so pixels can be more 
easily distinguished from one another, especially during processing. One advantage to 
Quickbird imagery is that there is a revisit time between 1—3.5 days, so multiple images 
can be obtained. Quickbird orbits at a height of 450 km. 

Prices vary for Quickbird imagery. Ordering directly from Digital Globe, archived 
natural color imagery costs US$10 per km , with a discount of 20 percent applied to 
academic research projects. If researchers on a project need multispectral or pan sharp- 
ened data (a process that transforms lower resolution multispectral imagery to higher 
resolution color data via georegistering pixels), the imagery becomes more expensive, 
ranging from US$14— 28 per km . There is a minimum order area of 25 km for library 
imagery, and 64 km for new imagery. If imagery needs to be specially obtained because 
it is not available from the image library, or if the imagery there is not sufficient, then 
costs rise. Prices vary depending on the total area to be taken and the type of imagery 
needed. Rush orders can double the price of imagery, but orders are not always able to be 
rushed if there are a number of orders already "in line." If imagery needs to be specially 
georeferenced at a scale of 1 : 5000, with Digital Globe responsible for taking points, costs 
can escalate to tens of thousands of dollars (see Many of 
the additional processes (pan sharpening, mosaicing, color balancing, and tiling) can be 



done through remote sensing programs, and archaeological teams can save money by 
performing these additional processes on their own. 

IKONOS imagery is similar to Quickbird. IKONOS imagery was launched by Space 
Imaging (now GeoEye) in 1999, and has a resolution of 0.8 m for color imagery and 
3.2 m for the near IR band. IKONOS imagery is used in many of the geosciences. 
With a swatch width of 1 1.3 x 11.3 km, IKONOS imagery can be ordered at 50 km 2 
for US$7.70, or orthorectified at US$13.20 km . Specifying areas to be imaged adds 
additional costs. While there is not an academic discount per se, researchers can negotiate 
with GeoEye for discounts. IKONOS orbits at 681 km, and records data at 10.30 a.m. 
each morning with a three-day revisit time. Like Quickbird, it records data at 11 bits. 

Many archaeological projects have used IKONOS and Quickbird imagery (Fowler 
1999; Hoffman 2001). Both datasets can be used to view architecture with a greater 
than 0.6 m resolution (or less if the architecture is spectrally distinct from the landscape 
surrounding it). If specific crop types are growing on top on the architecture, the near- 
IR band should allow researchers to detect them. Both imagery types are useful for 
detecting buried walls which have absorbed significant amounts of moisture or are 
made of materials (stones, mudbrick) that can be detected from space (Figure 3.20a and 

One team used both datasets to interpret a landscape in Norway, and then checked 
the data with geo-chemistry and excavation (Gron et al. 2006). Another team utilized 
IKONOS imagery to define damaged areas from the Vietnam War in the Plain of Jars, 
Laos (Box 2002). Archaeologists have not yet applied a full range of remote sensing 
techniques to IKONOS and Quickbird imagery, thus, future studies will undoubtedly 
have much greater success using them in their archaeological projects. 

IKONOS and Quickbird imagery have distinctly more advantages than 
disadvantages. With high-resolution data, they can be used in mapping exposed archi- 
tecture on many archaeological site types. This data can be tied into any existing grid 
systems, with the imagery corrected accordingly. High-resolution data can be draped 
over SRTM data to obtain virtual reconstructions of landscapes. Many remote sensing 
techniques, discussed in Chapter 4, have yet to be attempted on Quickbird and IKONOS 
data. Like all remote sensing analysis, there is no one way or automated procedure to use 
to detect ancient features, even with high resolution data. Each landscape will present its 
own challenges, with geology and archaeological features specific to a time and place in 
history. Thus, archaeologists must adopt different approaches. Cost can be a major pro- 
hibitive factor, but with educational discounts, Quickbird and IKONOS data represent 
the best value high-resolution imagery currently available. 


Advantages: near global coverage; can see beneath sand and rainforest canopy 
Disadvantages: need remote sensing programs for multispectral use; difficult to open 
Features: buried features (roads, rivers) can have associated archaeological remains 

alongside or near them 
Resolution: 15—45 m 
Accessibility:; ; 

Cost: SIR-C US$50 (three scenes); X-SAR US$40 per scene 





Figure 3.20 Quickbird images of Tell el-Amarna (scale 1:184 m): (a) showing housing in the 
central city; (b) using filtering to make subsurface architecture appear more clearly, 
2005 imagery courtesy of Google Earth™ Pro. 



SIR-A, or Shuttle Imaging Radar-A, was carried aboard the Space Shuttle Columbia in 
1981, carried a singly -polarized (HH: horizontal send and receive) L-band (23.5 cm 
wavelength), which included a synthetic aperture radar. SIR-B, launched on the 1984 
Challenger mission, had an L-Band and was HH polarized like SIR-A. The Sir-C/X-SAR 
missions were conducted aboard the US Space Shuttle Endeavor in April and September 
of 1994, covering 230 test sites around the world. The missions represent a cooperative 
effort between NASA/JPL (Jet Propulsion Laboratory), DARA (Deutsche Agentur fur 
Raumfahrtangelegenheiten, German Space Agency) and ASI (Agenzia Spaziale Italiana, 
Italian Space Agency). Advances in the SIR-C/X-SAR included an L-band and C-band 
(5.6 cm) radar and a X-band (3.1 cm) single polarization (VV: vertical send and receive) 
radar. SIR-C data can be ordered through the USGS data gateway server, while the X-SAR 
data can be ordered through the DLR-DFD in Germany on their webpage (Blumberg 
1998; Blumberg et al. 2002) at a cost of US$40-50. With a ground resolution of 15-45 
m, each SIR-C/X-SAR scene covers an area between 1 5—90 km (listed above). Additional 
commercial radar systems include ERS 1, ERS-2, JERS-1, RADARS AT, and ENVISAT, 
which have varying resolutions and band data. 

Radar imagery has many advantages and disadvantages for archaeological use (Elachi 
1982; Fowler and Darling 1988; Holcomb 1992). Radar imagery is used to detect a 
wide range of sites and features ranging from natural to human-made, including trails, 
roads, and canals. For a low cost, archaeologists can obtain high-quality imagery, but 
the limited coverage of radar imagery is an issue. Commercial radar data can cost many 
thousands of dollars (beyond the budget of most archaeologists), while SIR-C/X-SAR 
requires specialized training for interpretation. Radar imagery can have significant relief 
distortion, and there can be issues with feature interpretation. Wet surfaces (including 
soil and vegetation) will cause pixel scattering. Resolution ranges from 13 to 25 m, and 
like other imagery, can have sub-pixel feature detection. Many errors appear in radar 
imagery, including speckle noise (making features difficult to view), and the imagery 
must be georeferenced. Sun angle and shaded relief are more important to radar imagery, 
and altering them on remote sensing programs can cause additional features to appear. 

SIR-A (Figure 3.21) and SIR-B 's best-known application in archaeological work was 
the discovery of the so-called "radar rivers" in Egypt's western desert (McCauley et al. 
1982; McHugh et al. 1988; Holcomb and Shingiray 2007). Unlike conventional satel- 
lite or aerial photographs, radar images are not affected by atmospheric or sunlight 
conditions and can see through clouds and smoke. Using three bands of data (the SIR-C 
instrument contains the C and L-band radars, while the X-SAR contains the X-band 
radar), it is possible to gain information on a wide range of geophysical phenomena. 
While soil can limit penetration of the ground to a few centimeters, using the L-band 
in dry (desert) conditions makes it useful to see up to several meters below the earth's 
surface. This method is particularly useful to archaeological missions looking at ancient 
water courses, roads and sand-covered architecture. Longer wavelengths mean a deeper 
penetration of the ground to detect subsurface features. The best combinations include 
using the L-band and C-HH. SIR-C imagery aided in identifying alluvial fans in the 
Gobi desert of Mongolia, thus showing where archaeologists could look to locate addi- 
tional archaeological sites (Holcomb 2002). SIR-C imagery cannot be used globally due 
to issues of coverage. X-SAR imagery also helped researchers to detect previous Nile 
River channels around the area of the great Nile Bend near Luxor (Stern and Salam 1996). 
Only certain sections of Egypt had SIR-C/X-SAR mission coverage, including areas of 



Figure 3.21 SIR-A image of Egypt, 1981 image courtesy of NASA. 

the eastern Delta, Sinai and Nile Valley, thus restricting where archaeologists could 
apply radar imagery. Radar imagery is excellent for regional survey, but archaeologists 
need access to digital imaging processing software to open the imagery. Additionally, 
researchers must be aware of the types of error potentially encountered and the tech- 
niques (such as georeferencing, smoothing, speckle reduction) that should be applied in 
order to obtain the best results. 


Advantages: can view subtle landscape changes; high-resolution feature detection 

Disadvantages: high cost; not possible to fly everywhere in the world 

Features: can detect field patterns, architecture and other archaeological features not 

visible on aerial photographs; very high resolution data can detect features not visible 

on other satellite images 
Resolution: 3 cm 

(US projects); or 

html?lang=_e (UK/European projects) 
Cost: depends on project 

LIDAR (for Light Detection And Ranging) provides high resolution detail on features 
beneath the ground. The detail provided by such images is unparalleled and will open 
up many new avenues for archaeological research, perhaps allowing for detailed map- 
ping that, until this point, has been limited to aerial photographs, ground penetrating 



radar, magnetometry, and resistivity surveys. LIDAR data is recorded from a device on 
an airplane, but can be flown on smaller airborne devices such as ultralights (small, 
lightweight, single, or double-person aircraft). The LIDAR system measures distances 
to the ground through a pulse of light, taking 20—100,000 points per second. Points 
are calculated using a GPS system, with heights displayed with color coding. With 
the ability to operate at a rate of 35 scans per second, LIDAR records strips 450 m 
wide. Each flight path has a lateral overlap to make sure an entire area is covered. 
Landscapes can be simultaneously recorded with a vertical survey camera or airborne 
digital sensor, making LIDAR a companion to aerial photography when singular shots 
are required. 

The cost of LIDAR depends entirely on the length of time a system is needed, or if a 
team has to rent a plane and a pilot. Cost also depends on the total area being mapped, 
and the country where the project is taking place. It is possible to purchase flight times 
from commercial companies, but again, this can be expensive. Many countries in the 
world will not allow LIDAR to be flown due to military restrictions, so this must be 
taken into consideration when planning projects. The cost is generally in the range of 
thousands of dollars, but each commercial company will have varying rates. 

LIDAR imagery has helped archaeologists in many ways, most specifically in allowing 
them to map with high accuracy in a time efficient manner. In a 2001 study on Stone- 
henge (one of the most well-mapped archaeological landscapes in the world), LIDAR data 
helped archaeologists to make locational corrections to known sites (with an accuracy 
of 15 cm), detect new sites, including field systems, cross banks and slight earthworks, 
digitally eliminate surface features (trees and buildings) to create digital terrain models, 
and examine topographic relationships (Holden et al. 2002; Shell 2002; Bewley et al. 
2005) In the Vale of Pickering, UK, the team found that an absence of crop marks did 
not equal an absence of archaeological features, with many additional features found 
using LIDAR (Powesland 2006, 2007). In the Loughcrew region of Ireland, LIDAR 
aided archaeologists in identifying previously unknown archaeological features (Shell 
and Roughley 2004). In Brittany, France, LIDAR has allowed archaeologists to visu- 
alize the landscape surrounding the well-known site of Carnac (Roughley 2001, 2002, 
2004; Roughley et al. 2002). Each archaeological site requires testing the LIDAR at 
different heights based on the landscape types. 

Using LIDAR, archaeologists can create either a digital terrain model or a 3D visual- 
ization of landscapes. Archaeologists can also pre -program the LIDAR system to collect 
data with reference to any global grid or coordinate system. One test of LIDAR data 
occurred in the River Trent regions in the English Midlands for the Rivers Ouse, and 
Foss in the Vale of York, North Yorkshire and River Witham in Lincolnshire. The 
archaeological team wanted to test the utility of LIDAR data in an alluviated landscape 
to examine geomorphological developments and to model past flooding. Combining 
the LIDAR data with aerial photographs allowed for comparative data analysis. The 
LIDAR system was connected to a differential GPS system, allowing for ease of data 
reconstruction. LIDAR data allowed the team to examine how humans had modi- 
fied the overall landscape. Terraces appeared clearly, along with past creek ridges. The 
team also found that peat deposits had once covered the entire landscape, and that 
palaeochannels had higher soil moisture and additional organic debris. Future applica- 
tions with LIDAR might take place in landscapes where features are only marginally 
different than surrounding landscape, or in areas where aggregate extraction, housing, 



and development affect landscapes (Challis 2006; Challis and Howard 2006; Challis 
etal. 2006) 

Other airborne sensors: RADARS AT, airborne thermal 


Advantages: can see beneath cloud cover and vegetation 

Disadvantages: high cost, limited global data 

Features: possible to identify roads, pathways, and entire sites in rainforest areas 

Resolution: 3 m (RADARSAT-2), 8-30 m (RADARSAT-1); 2.4-13.7 m (SAR) 

Accessibility:; http://gs.mdacorpo 

ration . com/produc ts/sensor/radarsat/rs 1 _price_ca. asp 
Cost: RADARSAT-1 Archived imagery US$1500; other imagery US$3600-4500; 
RADARSAT-2 depends on scene size, higher cost for additional processing and rush 
orders; ATR depends on scene size, must be worked out with NASA 

RADARS AT and AIRSAR are SAR (Synthetic Aperture Radar) satellites, both with 
similar capabilities. RADARS AT is a commercial satellite controlled by the Canadian 
Space Agency, while AIRSAR belongs to NASA. RADARSAT 1 provides horizontal- 
transmit and horizontal-receive (HH) data, while RADARSAT-2, launched in 2008, 
provides VV polarization, cross-polarization (HV or VH), dual-polarization (HH + HV 
or VV+VH) and quad-polarization (HH+VV+HV+VH). This makes RADARSAT an 
incredibly versatile imagery type. RADARSAT collects data in swaths of between 10 and 
30 km, with a resolution of 3 m. Pricing for RADARSAT 1 ranges from US$1 500 for an 
archived scene (prior to 2001) to US$4500 for an 8 m resolution scene. RADARSAR-2 
costs vary depending on the overall image size, while the cost for flying an AISAR 
mission must be worked out with NASA pending details required and the size of the 
area to be imaged. 

First flown in 1988, and flown since once per year, AIRSAR (for Airborne Synthetic 
Aperture Radar) is a NASA all-weather imaging tool. Like SIR-C data, AIRSAR can 
see beneath clouds and obtain night data. The data also is useful in dry snow, thin 
sand, and in areas with dense forest canopies. Flown on the NASA DC-8 sub-orbital 
platform, the AIRSAR has an accompanying lab and crew. The AIRSAR mission has been 
flown primarily over Central and South America and Antarctica, where it mapped large 
forested areas, oceans, hydrological patterns, and a number of natural hazards. AIRSAR 
utilized polarimetric SAR instrumentation, first demonstrated on the SIR-C mission. 
AIRSAR will achieve 1.5 m resolution in future missions. The C and X bands of SAR 
are sensitive to surface microtopography, and the P and L bands can penetrate both soils 
and vegetation (adapted from 

SAR datasets, with the ability to record data beneath the Earth's surface, have been 
applied to a number of archaeological investigations. In the Central Iberian Peninsula of 
Spain, SAR data (with a 2.4—13.7 m resolution) found potentially buried architecture 
(Ayuga etal. 2006). SAR data has also been used to detect archaeological sites not discov- 
ered during foot survey. In examining structural patterns at Petra, Jordan, SAR detected 
previously unknown linear features. The data also showed ancient pathways, open subter- 
ranean chambers, and natural landforms related to known archaeological sites. This study 



continued at Beidha, Jordan though a cultural site analysis initiative, which identified 
the general landscape condition of the area (Comer 1999). On San Clemente Island, in 
California, AIRSAR Data (with resolutions of C-band 5.7 m, L-band 23 cm, and P-band 
67 cm), aided in surveying Department of Defense (DoD) land. Eighty percent of DoD 
land remains unsurveyed. With newly located sites imported in the DoD GIS database, 
land management agencies can be better informed and Cultural Resource Management 
(CRM) guidelines can be changed (Comer and Blom 2002). AIRSAR has also been 
used extensively at Angkor Wat to understand more complicated human— environment 
interactions (Evans et al. 2007). 

Airborne thermal radiometry involves the use of a thermal sensor to detect buried 
objects via significant heat flux, or absorbed heat from the sun. Non-heated objects can 
be more difficult to locate. Features are visible based on their depth, vegetation cover, the 
time of year, and the environment in which they are located. The thermal radiometer can 
be placed on either a helicopter or an airplane, with the video detectors sensitive to the 
visual, near IR, and thermal IR parts of the spectrum. Work done at an early Bronze Age 
site in Israel, Leviah Enclosure (not seen on the ground or in aerial photographs), was 
detected with ATR and later confirmed with excavation (Ben-Dor et al. 1999, 2001). 
Archaeologists have found ATR helpful for locating ancient road networks in Costa 
Rica due to the thermal properties of the roads (Sheets and Sever 1991). As with all 
remote sensing, timing is everything when using ATR. Teams must decide what the 
most advantageous time might be for recording differences in heat beneath the soil. 
This is generally in the early mornings when the upper soils are cool and the lower soils 
are warm, thus creating a maximum thermal gradient (Ben-Dor et al. 1999). 

Data quality 

When preparing for a remote sensing project, and during the process of remote sensing 
analysis, each team must assess overall data quality versus budgetary restraints. A great 
deal can be accomplished with free or low-cost imagery, yet what are the tradeoffs? 
Quickbird imagery represents the highest resolution data, and ordering a small area 
from the archives of each imagery type only costs around US$250. If one has even a 
limited budget (less than US$1000) for purchasing imagery data, this is well within 
budgetary restraints; combined with free data sources, this can allow for a detailed 
remote sensing analysis. Data at a 30 cm resolution will soon be available, with higher 
resolution satellites on the horizon for the next three to five years. 

With high-resolution data available at a generally low cost, why should archaeologists 
use Landsat data? Should they be required to learn about other satellite imagery types 
aside from high resolution data? Future archaeological analysis will require detailed 
assessments of landscape changes. If one does not know how to assess imagery from the 
1970s and 1980s, one cannot evaluate landscape changes. Archaeologists rely a great deal 
on archived data, whether through historical sources or old maps. Assessing historical 
information often requires specialized training; this is true also for Landsat and other 
older multispectral satellite data essential for any remote sensing studies. Learning how 
to assess the advantages and disadvantages of each imagery type was the focus of this 
chapter, but additional advantages and disadvantages may become apparent as the field 
becomes more developed. How archaeologists use and integrate multiple satellite image 
types as possible into their research is also an important factor. 



Advances in data quality will affect how archaeologists approach remote sensing 
analysis (Failmezger 2001). One pixel of MODIS satellite imagery (at a resolution of 
lxl km) is represented by 27,778,889 Landsat pixels, while one pixel of Landsat 
imagery (30 x 30 m) is represented by 2500 Quickbird pixels. The scale of the data 
is something to think about during the process of imagery analysis. One wonders if 
archaeologists in 100 years will be using Landsat data, or if future satellites will be able 
to detect all landscape changes through time. Regardless of advances, archaeologists will 
need to learn basic remote sensing skills in order to analyze data for their research. Google 
Earth™, World Wind and other online imagery viewing programs will keep advancing, 
and it is difficult to predict how much automated remote sensing "analysis" one will 
be able to do in 5, 10 or even 20 years. Ultimately, everything depends on individual 
projects, landscape types, and the remote sensing techniques researchers hope to apply 
in their research projects. 




Once archaeologists have obtained suitable satellite imagery, the next logical step is 
imagery analysis. Without taking an introductory remote sensing or GIS course (through 
a university or an online course, see, and without access to 
commercial or free remote sensing and GIS programs, it will not be possible to use 
many of the techniques to be discussed in this chapter. However, visual identification 
techniques will be discussed, as well as what can be seen with basic imagery enhancement. 
A project may already have a remote sensing specialist. If so, then this chapter will help 
the remote sensing specialist choose appropriate techniques of analysis and will assist 
archaeological teams in understanding how different remote sensing methods work on 
ancient sites and features, seen in Table 4.1. Students may find that the discussion of 
techniques can aid in their project work. As so much of the world remains to be analyzed 
from the perspective of satellite archaeology, trying as many analytical techniques as 
possible (within a feasibile timeframe) may be a good choice. One will not know the 
techniques that will work best, given the variables affecting imagery interpretation. 
This includes weather, anthropomorphic factors, as well as image spectral, spatial, and 
temporal factors (Figures 4.1a and 4.1b). 

Prior to discussing processing applications for satellite imagery, it is necessary to differ- 
entiate satellite imagery from another from of remote sensing which is no less important, 
which is geophysics. Satellite remote sensing covers large areas of the Earth, while geo- 
physical instruments are run along the Earth's surface and look below the ground surface 
in concentrated areas. Unlike most satellite satellite remote sensing, geophysical inves- 
tigations can go deeper beneath the Earth's surface. Wide-scale satellite remote sensing 
survey, magnetometry, and ground-penetrating radar (GPR) (Conyers 2007; Goodman 
et al. 2007) may be similar in purpose (i.e. locating archaeological materials hidden to 
the naked eye), but differ in their scale and the ways in which the results can be obtained. 
Understanding these differences is important when designing field and survey seasons, 
and determining which technology is most appropriate to apply. The four main geo- 
physical instruments include resistivity, magnetometry, electromagnetic induction, and 
GPR (Wynn 1986; Meats and Tite 1995; Meats 1996; Marcolongo and Bonacossi 1997; 
Sarris et al. 1998; Sarris and Jones 2000; Leucci 2002; Lorenzo et al. 2002; Conyers 
2004; Sarris 2005; Kvamme 2007; Roosevelt 2007; Sarris et al. 2007; Williams et al. 
2007; Ernenwein and Kvamme 2008). They are tools archaeologists have used to see 
subsurface ancient structures (Becker and Fassbinder 2001; Herbich 2003), including 



Table 4. 1 Techniques for imagery analysis per main imagery type used in archaeology 

Aerial Corona Landsat SPOT ASTER Quickbirdl SIR-CI SRTM 

graphy IKONOS X-SAR 






Land use land 

cover change 
DEM creation 















































X X 





Figure 4. 1 Mille Lacs Lake, National Wildlife Refuge, Minnesota, Landsat images from: (a) May 
2001; and (b) July 2001 (scale 1:10432 m) note the difference in vegetation cover 
between May and July, images courtesy of NASA. 

temple enclosure walls (Pavlish 2004), houses, tombs, and palaces (Pavlish et al. 2004), 
and have even helped to map entire cities (Becker and Fassbinder 1999). They are cru- 
cial tools within archaeology and should not be overlooked when dealing with the wide 
range of available tools for surveys and archaeological excavations (Dolphin et al. 1977; 
Giddy and Jeffreys 1992; Becker 2001; Rizzo et al. 2003; Kvamme 2008). 



Resistivity, magnetometry, and GPR surveys can detect subsurface features with a 
reasonable degree of accuracy and have a spatial resolution matched only by the most 
expensive satellite images (Quickbird and Spin-2). These tools are best applied to 
small areas and would require prohibitive labor costs and time for a large-scale sur- 
vey. Furthermore, specially trained scientists and archaeologists are required to conduct 
magnetometry, resistivity, and GPR surveys, to operate equipment, and address techni- 
cal problems. Inexpert handling of the equipment and diverse situations can introduce 
irreparable error into crucial data. The overall cost of purchasing such equipment, and 
the need to upgrade every few years, can also be a deterrent. For instance, fluxgate gra- 
diometers cost many thousands of dollars. Hence, projects can borrow geophysicists from 
universities, along with their equipment. Other projects rent equipment and bring out 
specialists from surveying companies, which may still be expensive depending on daily 
rental and stipend rates. Depending on individual projects, surveying can be more time 
consuming than expected, especially with equipment failures. 

Satellite remote sensing techniques can provide the exact location of surface sites prior 
to more detailed magnetometry, GPR, and coring surveys. Magnetometry, resistivity, 
and GPR surveys are best used once archaeologists have found archaeological sites and 
want to gain detail about subsurface structures before excavation or in place of excavation. 
Is it more crucial to locate as many archaeological sites and features as possible before 
they disappear from plain view, in order to facilitate future ground-based surveying? 
The best answer is to increase the scope of work to cover both approaches, or, failing 
funds and personnel, try to record as much as possible before things vanish completely. 
If archaeologists know the locations of new sites found using satellite remote sensing, 
they can return to do additional subsurface geophysical ground surveying in future 
seasons. Attempting a limited number of algorithms over a 12 -month or seasonal set 
of the same free or inexpensive satellite images allows for comparison, especially if 
an individual or team has not visited the region in question. One method may work 
particularly well in drier seasons, while other methods may work well in wet seasons. 
Landscape types will also influence what techniques to use. Prior to ordering more 
expensive high-resolution or SAR imagery, an individual or team should know from 
what time of year to order imagery. With budgets for archaeological work becoming 
even more restricted, with limited funding available for undergraduate or graduate 
projects, students and professional archaeologists need to have as focused an approach 
as possible. Papers that evaluate a limited number of methods will be valuable for other 
projects in the same geographic region. Field survey work may allow the team to identify 
additional methods to apply, or to refine already applied methods even further. 

Some surface features identifiable from space may appear seasonally, monthly, or over 
an even shorter period of time. This is why understanding local landscapes and their 
seasonal variance is critical to the success of remote sensing analysis. Cropmarks appear 
during the initial growth of crops or vegetation, when their growth period is fastest. 
This will vary as per crop type. Frost may act like cropmarks and make certain features or 
sites appear due to heat absorption, or, in a reverse situation, water may melt snow or ice 
faster or slower atop archaeological remains (Myers 1989). For example, during survey, 
a team may discover that a specific type of shrub grows on top of ancient walls during 
March to May of each year. This shrub may have its own distinct spectral signature that 
can be identified using high-resolution Quickbird imagery. Coarser resolution ASTER 
or Landsat imagery may or may not detect this shrub type. Even though initial analysis 



might not have detected the shrub, future analysis with higher resolution imagery from 
a specific time of year should detect it and contribute to future seasons. 

Remote sensing experts can never predict which analytical methods will work best 
for project-specific analysis of satellite imagery. Even if a particular technique worked 
on a SPOT satellite image from the spring of 1995 to identify a buried river channel 
in a floodplain, the same technique might not work on a SPOT image from the spring 
of 2005. Atmospheric conditions change, weather patterns are variable, and a great 
deal can affect a landscape over the course of 10 years. Perhaps farmers reclaimed a large 
portion of the floodplain, or the government decided to start a massive relocation project 
to construct a new city in the floodplain area. One might attribute the location of the 
river channel in 1995 to heavy snowfall during the winter that left the floodplain wetter 
than usual, with extra soils clogging the channel and absorbing the additional ground 
moisture. SPOT imagery may exist from the spring of 2005, but with significant cloud 
cover, rendering it useless for project work. All ancient remains leave distinct spectral 
signatures on the landscape, yet each satellite image is a scan of the landscape taken at a 
particular time with various spectral and spatial resolutions that may detect the remains 
in a different way. Additionally, current satellite imagery may not be able to detect each 
type of buried or partially exposed ancient sites and features. 

Each remote sensing specialist approaches imagery analysis from their own perspective 
and will "see" ancient remains in a different way. Should we trust imagery interpreta- 
tion more from an archaeologist with limited remote sensing experience and 30 years 
of survey and excavation experience, or is a remote sensing specialist with 30 years of 
imagery processing experience and a limited archaeological background more reliable? 
Each expert will have different ideas for how to approach the imagery. Archaeologists 
are likely to view landscapes in their totality, combining ancient and modern land- 
scape perceptions and searching for greater meaning in the findings. Remote sensing 
specialists often concern themselves more with meaning on the level of the pixel, 
asking why specific pixels appear in a certain way. Both approaches are invaluable, 
and show why remote sensing specialists and archaeologists should collaborate as much 
as possible. 

Imagery processing programs 

There are a number of remote sensing and GIS programs that allow archaeologists to 
conduct remote sensing analyses. They are similar in that they allow the user to open 
and use a wide variety of satellite imagery as well as apply different algorithms. Their 
cost will vary depending on the use: Commercial licenses are far more expensive than 
educational licenses and both depend on the number of licenses ordered. If an institution 
already holds licenses for remote sensing or GIS programs, it may be possible for students 
to obtain a student license for a nominal fee. ER Mapper (Earth Mapper), PCI Geomatica, 
ERDAS Imagine®, and ENVI are the most common remote sensing programs, with the 
majority of introductory remote sensing classes using ER Mapper, ERDAS Imagine®, 
or ENVI. Most GIS courses will use ArcGIS® . Some remote sensing programs are more 
conducive to advanced remote sensing processes than others. Prior to purchasing imagery 
software, one should consult with the dealer to see if the program will work with lab or 
personal computers, and will be able to process the required imagery. All of the remote 
sensing programs mentioned have a free 30-day trial period. 



Free downloadable remote sensing programs are available, but they do not have 
the total processing capacity of the aforementioned programs, or the ability to run as 
many algorithms, although they can be run on both Macintosh and PC computers. 
Using commercial remote sensing programs, archaeologists and remote sensing spe- 
cialists have the ability to patch together and compress large satellite datasets, 
program their own algorithms, save and export imagery in a wide range of for- 
mats, process RADAR imagery, and analyze data at the pixel level. Multispec© (see^biehl/MultiSpec/), a program available from Purdue 
University, is compatible with either Macintosh or Windows operating systems. The 
programs can read .Ian and .hdf image extensions (which are the formats for most free 
Landsat and ASTER data), and have the ability to run different image classifications as 
well as contrast enhancement. GRASS (see, a combined GIS and 
remote sensing program designed initially by the US Army Construction Engineering 
Research Labs, is a much more versatile program than Multispec. It has many GIS and 
remote sensing capabilities, offers many remote sensing analysis tools described in this 
chapter, and can open a wide range of file types for Landsat, SPOT, ASTER, and SAR 
images. Both Multispec and GRASS have online user manuals and discussion boards, 
making them good options for archaeologists who do not have the funding to purchase 
the previously mentioned commercial remote sensing programs. 

If archaeologists wish to make maps, or just open satellite imagery for visual inspec- 
tion, a number of free imagery viewing programs exist online (with websites current as 
of Jan. 1, 2009). They include: 

• MrSID GeoExpress View ( 

• ER Viewer ( = 1 60) 

• Geomatica Freview ( 

• ERDAS Viewer (requires registration): ( 

• ENVI Freelook (http://rsinc. com/download/download. asp?searchstring=Free Look& 

Each program allows the user to view satellite imagery in restricted formats, zoom in 
and out, create maps, adjust contrast, and combine different bands of spectral data. It is 
advisable to archaeologists and students without previous remote sensing experience to 
open free satellite imagery in any of the aforementioned programs. This will allow some 
basic imagery processing to take place. As many archaeological features have seen during 
the process of visual examination (see below), one never knows what might appear. 

Integrating satellite images and aerial photographs, 
and visual interpretation 

Aerial photographs can certainly contribute to remote sensing projects. What happens 
when remote sensing datasets are treated as either high resolution or multispectral 
photographs? There are many analytical techniques archaeologists and remote sens- 
ing specialists can use on imagery, but the initial visual inspection of the imagery is 
critical. Archaeologists gain a "lay of the land," and assess imagery for further analysis. 



Much can be gleaned from a pure visual inspection of satellite imagery, which is pos- 
sible on any free image viewing program. Irfanview (see http://www.irfanview.eom/_) 
is one of the most versatile image viewing programs available, and allows users to 
enhance imagery in a variety of ways. Archaeologists have discovered many ancient 
features treating satellite images as high resolution aerial photographs. When this 
happens, archaeologists need to address their advantages and disadvantages, much as 
one would do with aerial photographs (as described in Chapter 2). As in all imagery 
interpretation, much remains in the eye of the beholder (Rapp 1975; Ebert and Lyons 
1980a, 1980b). 

One of the most straightforward ways to enhance an image for visual inspection is 
to apply a contrast stretch through histogram equalization. This will highlight certain 
features in the image. Each pixel in a satellite image can range in value from 0—255 
(digital numbers, or DNs). This 256-number range refers to 8-bit imagery, or 2 power. 
11 -bit imagery will have a value of 0—2047, or 2 power. The contrast stretching 
expands the brightness values over a bigger range so that certain aspects of the image 
appear in greater contrast to one another (Lillesand et al. 2004: 492—9). Histogram 
equalization is an important step in the preparation of satellite images for analysis. 
Not only must they be georeferenced, but also they must be prepared in a way that 
optimizes recognition of important features. Allowing for contrast enhancement makes 
this possible, shown in Figures 4.2a and 4.2b. 

Enhancement of Corona high-resolution satellite photography has allowed archaeolo- 
gists to discover past road systems and old canals with visual detection in landscapes now 
largely vanished in Syria and Egypt. Landsat, SPOT, and ASTER imagery have resolu- 
tions too coarse to detect many small ground features, while Corona imagery, especially 
the KH-4B series, reveals features as small as 1—2 m thick. Largely invisible due to 
landscape changes, Corona imagery has allowed the detection of Sennacherib's northern 
Asyrian canals (Ur 2005), and a number of ancient road systems (called "hollow ways") 

4.2 Belize coast, Landsat images: (a) with; and (b) without contrast stretch (scale 
1:11200 m), images courtesy of NASA. 



Figure 43 East Delta, Egypt, Corona image, image courtesy of US Geological Service. 

between settlement sites in northeast Syria. Some hollow ways are visible today on high- 
resolution satellite imagery (Emberling 1996), but moisture in the soil of these ancient 
roadways allowed them to be distinguished on the Corona imagery (Ur 2003, 2006). In 
Egypt, visual inspection of KH-4B Corona imagery, seen in Figure 4.3, confirmed the 
location of previously unknown ancient sites in Middle Egypt and the Delta, and tracked 
the destruction rates of nearly 200 known settlement sites (Parcak 2004b, 2007a). 

Analyzing satellite imagery through visual inspection has advantages and 
disadvantages. Control points for older imagery can be hard to obtain, especially where 
modernization has greatly altered landscapes. A lack of good documentation can also 
hinder research. This is what makes georeferencing, or putting the imagery in a known 
mapping reference system, so important. One study at the Roman town of Corinthia, 
located in the southern part of Austria, combined aerial photography with Corona 
high-resolution space photography over a 23,000 m area. The team used the Corona 
imagery to create a DEM, and revealed a dense settlement area that was connected with 
excavated samples (Doneus 2001). Draping aerial photographs over Corona imagery 
to create DEMs is one low-cost way to reconstruct older landscapes (Dengfeng and 
Dengrong 2004). Another study compared aerial photography with airborne thematic 
mapper imagery (which has 1 1 bands ranging from the visible to the NIR). The study 
examined seasonality of imagery with ground moisture and ground temperature, and 
found that timing was one of the most important factors for all the examined imagery. 
Linear anomalies were observed in June, while old field boundaries appeared in August 
but not in March. Thermal inertia, or the ability of buried features or cropmarks to 
reflect heat differently, proved to be another major factor (McManus at al. 2002). 



Figure 4.4 North Sinai Lagoon system, Egypt (scale 1:4560 m), arrow points to paleo-lagoon in 
western part of the image, 2008 image courtesy of Google Earth™ Pro. 

The timing of imagery also affects how archaeologists can visually interpret Corona 
and high-resolution imagery such as IKONOS and Quickbird. Visual inspection of past 
imagery datasets can aid in reconstructing entire landscapes, and allows for insights into 
key historical periods of time. In Egypt's New Kingdom (ca. 1550—1070 bc), a series of 
fortresses dotted the "Ways of Horus," or the North Sinai road between Egypt and their 
eastern frontier. One team used Corona high-resolution space photography to reveal the 
location of a fort along the edge of an ancient lagoon system (seen in Figure 4.4), which 
helped in reconstructing New Kingdom military routes (Moshier and El-Kalani 2008). 


Most satellite images, when ordered or downloaded from the Internet, are georefer- 
enced, or mapped to a specific projection system (Figures 4.5a and 4.5b). Corona 
imagery, aerial photographs, and some high-resolution datasets may not be georefer- 
enced, necessitating the selection of ground control points for correcting the imagery. 
This process has a number of names: Georeferencing, georectification, image rectifica- 
tion, or image correction. Ground control points can be obtained from good maps of 
an area, or another satellite image that has already undergone georeferencing. Thanks 
to Google Earth™, World Wind, and Landsat imagery on MrSid Geo Viewer, remote 
sensers have access to global ground control points of varying resolutions. Error rates 
(called "root square mean error", or RSME), or point offset, will vary depending on 
the type of imagery. This refers to the distance than any pixel will be "off" the actual 


Figure 4.5 Georefe retiring, compare: (a) non-georeferenced 1972 Corona image of Dikirnis, East 
Delta Egypt; with (b) 2008 Quickbird image of the same town, note the distortion 
in the non-georeferenced Corona imagery, images courtesy of US Geological Service 
and Google Earth™ Pro. 


ground location. During survey, if one is within 30 m of any site with RSME, the site 
or feature is likely to be located. During the 2002—5 survey seasons, the author found 
that georeferenced Landsat imagery proved highly accurate, and was more reliable than 
recent government maps made from georeferenced aerial photographs. 

How does georeferencing work? Normally a map displays geographical locations in 
latitude and longitude. This system, which originated in ancient Greece, is not with- 
out complications. Any map projection of the Earth is distorted in terms of distance, 
size, shape or direction, since one is attempting to place a curved, round surface onto 
a flat, 2D surface. Most satellite images are received as "raw" data without any coor- 
dinate system applied to them. Thus, any remote sensing analysis must choose which 
coordinate systems to use in a Cartesian, or an X— Y coordinate system, called a map pro- 
jection (Bugayevskiy and Snyder 1995: 1—17). Most remote sensing research expresses 
locations through eastings and northings, which are obtained by using a Universal 
Transverse Mercator (UTM) projection. Sixty UTM zones cover the Earth. The projec- 
tion is essentially a flat grid square applied against an elliptical point running along 
the surface of the Earth. The numbered part of the UTM refers to the local ellipsoid 
against which the projection is applied. Using a UTM system, the central point in the 
projection is accurate but the distortion grows as distances increase (Johnson 2005). 

This distortion is corrected in remote sensing programs through the selection of 
ground control points within a known reference system. Eastings and northings are 
figures used to obtain the exact location of a particular point. Eastings represent lines 
running from East— West and northings represent lines running from South— North. 
They are used instead of latitude and longitude when precision is required, something 
which is particularly useful in archaeological survey (Lillesand et al. 2004: 47—9). Six 
or more ground control points are generally needed to have a low root square mean 
error rate. Georeferencing can be done with any commercial remote sensing program, 
Airphoto, and Multispec©. After selecting ground control points, the user then connects 
the points to the same points in the non-corrected image. The computer program then 
corrects the original image through a process known as "rubber sheeting," or stretching 
certain portions of the non-corrected image to orient them within the same coordinate 
system. If coordinate points need to be changed, the site allows 
points to be exchanged within coordinate systems. 

Many archaeological projects use georeferencing to correct their Corona imagery or 
other non-georeferenced imagery as a standard first processing step. Not as many projects 
exist which have it as their only processing step, but georeferencing did aid historians 
in reconstructing the path of the Lewis and Clark expedition across the USA. During 
the expedition, Lewis used chain poles and other mapping and plotting tools to map 
their route, which had 600 campsites. Lewis estimated a total trip of 41 62 miles. 
Georeferencing allowed scholars to correct old maps of the expedition with Landsat 
data, and showed that Lewis and Clark's map was only 40 miles off- route (Landis 2004). 
Georeferencing Landsat 5 data also helped to reconstruct past land use patterns in 
Mallorca (Montufo 1997). Old maps may exist for regions that identify now "lost" 
archaeological sites, and georeferencing will help archaeologists to relocate those sites. 
Combining georeferencing with the examination of long-term landscape changes will 
aid in relocating former branches of river courses or lakeshores. Georeferencing is a 
valuable remote sensing method, yet in general is only the first step of many taken in a 
remote sensing study. 



Band combinations 

All multispectral satellite images have multiple bands of data, which remote sensers 
can use to make certain features stand out. Each satellite image type will have various 
numbers of bands and each band will show a different range of the electromagnetic 
spectrum. ASTER imagery has 15 bands, Landsat 7 has seven bands, SPOT imagery has 
four bands, while the EO-1 Hyperion has 220 bands. No more than three bands can 
be viewed at once on any satellite image program. By blending the different types of 
information visible on satellite images, it is possible to make vegetation and geological 
features not viewable in the visible part of the electromagnetic spectrum appear more 
clearly. Each band of data can be correlated with specific ground features, and this will 
vary on each image. This was described briefly in Chapter 3, but many other studies 
detail the full range of detectable features in every band of satellite imagery. 

The way most satellite images are viewed is as visible images, or a 3-2-1 RGB (red, 
green, and blue, referring to each of the three viewing bands in remote sensing programs 
compare Figure 4.6a to Figure 4.6b). Landsat satellite images can commonly be viewed 
as a 4-3-2 RGB, or IR-R-G (infrared, red, and green, each band viewed in the red, 
green, and blue bands). In this image type, vegetation appears as red because vegetation 
reflects more strongly as red in the infrared part of the electromagnetic spectrum (Jensen 
1996: 40; Lillesand et al., 2004: 5-9). There are a total of 343 possible combinations 
for viewing Landsat satellite imagery (7 bands x 7 bands x 7 bands), so attempting 
limited band combinations can be useful for archaeological projects. A 4-3-2 RGB also 
represents the band combination most commonly used for satellite imagery processing in 
archaeological analyses. This is because vegetation is so often connected to archaeological 
features. Other bands helpful for archaeological work are bands 4,5, and 6. In one survey, 
band 4 helped to pick out dark tones on the image that equated to round barrows in 
Turkey. These were visually confirmed though a KVR-1000 photograph. Combining 
these bands also helped to isolate canals, and clarified areas of vegetation in each region 
of study (Fowler 1996a; Comfort 1997b, 1997c). 

Figure 4.6 Macchu Piccu, Peru: (a) is a Landsat 3-2-1 RGB; and (b) is a Landsat 4-3-2, note that 
Maccu Piccu, the white area indicated by the arrow, appears more clearly in image (a), 
images courtesy of NASA. 



Even a 3-2-1 RGB visible image may pick out features from space not noticeable on 
the ground, and allow for general geographic details to be noted. The image will be "false 
color" which means that the pixel values will not be an exact representation of a true color 
aerial photograph. SPOT satellite images have 125 total possible band combinations 
(5x5x5 bands). Although the bands are in slightly different spectral ranges than 
Landsat imagery, combining them in multiple ways did not make any archaeological 
sites appear as distinct in a study in the northeast Delta of Egypt. In a number of cases, 
features such as crops became highlighted, but, much like in the Landsat imagery, the 
analysis did not make ancient sites stand out as distinct unless one knew exactly where 
to look (Parcak 2007b). 

How band combinations can best be used in archaeological projects is an area that 
needs much work. One archaeological study compared Landsat TM and SPOT satellite 
images in the Lasithi district of Greece and the ancient site of Zhouyuan in China. 
The researcher used both image types, with different band combinations to identify the 
spectral signatures of archaeological sites in Greece, identifying many Minoan period 
sites. Ground survey detected well preserved and poorly preserved sites, as well as a 
number of sites not visible. This contributed to the creation of a regional risk map. 
In Zhouyuan, Landsat TM imagery was used in combination with aerial photographs. 
A 4-7-5 Landsat TM image aided in identifying rammed earth used for building tombs 
and other structures (Sarris et al. 2002). Using band combinations other than a 4-3-2 
RGB for running algorithms may aid future archaeological remote sensing projects. 
Experimentation is the only way remote sensers will learn which band combinations 
work, and which do not. 

Normalized Difference Vegetation Index 

Normalized Difference Vegetation Index (NDVI) is a method for measuring vegetation 
vigor in satellite imagery. This analytical technique compares the IR and red bands from 
multispectral satellite imagery (the red band and the near-IR band) in the following 
formula: B4 - B3/B4 + B3, or NDVI = NIR - Red / NIR + Red. The closer the 
value of the formula is to 1, the higher the overall vegetation value, while the closer 
the value is to zero, the closer the landscape will be to barren. Other comparative 
techniques include ARVI and SAVI (Atmospheric Value Index and Shade Area Value 
Index, respectively), but do not generally apply as well to archaeological site and feature 
detection. The vegetation vigor may be associated with better general plant health, 
or may be associated with buried archaeological features affecting the health of the 
vegetation atop it. Vegetation values over broad landscapes fluctuate depending on dry 
or wet seasons (Schmidt and Karnieli 2002) and should be considered when using NDVI 
as apart of archaeological projects. Semi-arid or arid regions will have different vegetation 
signatures, with vegetation decomposing at varying rates (Hurcom and Harrison 1998). 
Rainfall and seasonality remain two of the most important factors for all remote sensing 

Archaeological features affect vegetation in a variety of ways. In some cases, com- 
pletely different vegetation types may grow atop archaeological sites, while in other 
cases the general vegetation signatures will be stronger (Saturno et al. 2007). Buried 
walls or decomposing organic matter may retain higher amounts of moisture and increase 
vegetation density in these areas. Using NDVI to examine broader human— environment 



interactions may aid in reconstructing past agricultural practices. Using low-resolution 
AVHRR satellite imagery (with a resolution of 1 km) over the course of a year, one study 
showed high NDVI values with the maturation of winter crops. The project revealed a 
range of complicated agricultural and irrigation practices, and compared the results to 
the location of known archaeological sites (Kouchoukos 2001). Studying overall capacity 
of landscapes with satellite based biomass production estimates may help some archae- 
ological projects with estimating past land use issues (Tieszen et al. 1997 '; Ganzin and 
Mulama 2002). 

NDVI can be used to study more modern issues that may have affected past archaeo- 
logical sites. In an exploration of ancient sites in Western China, one team used Landsat 
TM, SPOT, Quickbird, and IKONOS imagery to study the abandonment of the town 
of Tongwangcheng, a major Hun city. The town used to be located on the Wud- 
ing River, but as the river dried up, sands covered the city. NDVI helped the team 
in understanding patterns of desertification (Changlin and Ning 2004) (Figures 4.7a 
and 4.7b). On the other side of the world, NDVI assisted in studying the spectral 
response of archaeological sites in the Calakmul Biosphere reserve along the base of 
the Yucatan Peninsula in Mexico. The study compared known archaeological sites 
with random points using bands 4 and 5 on SPOT and Landsat imagery (Ostir 
et al. 2006). 

Italy has the largest number of archaeological studies examined using NDVI. The 
number of archaeological sites containing buried walls makes Italy an ideal region in 
which to test NDVI. Using MVIS imagery with 102 bands (ranging from visible to 
thermal IR) one team studied the site of Arpi in central Italy. Vegetation indices revealed 
the outline of the city, its moat, and many linear anomalies (Merola et al. 2006). At two 
test sites in southern Italy, one team used Quickbird imagery to locate several buried 
walls via cropmarks in areas with ground vegetation. The team compared images with 
dry vegetation and NDVI at Monte Issi and lure Vetere, both medieval sites in regions 
with Greek and Roman colonization. Overall resolution was noted to be lower than 

4.7 Western China, Tongwangcheng: (a) 3-2-1 RGB Landsat images; (b) NDVI 
(scale 1:2100 m), note the vegetation is brighter in image (b) and the archaeological 
site appears as white, images courtesy of NASA. 



aerial photography. Results were confirmed using geophysical prospection (Lasaponara 
and Masini 2006a, 2006b, 2007; Masini and Lasaponara 2006a, 2006b, 2007). 


Imagery classification is the most applied technique in archaeological remote sensing 
analysis through either supervised or unsupervised techniques (Forte 1993). The purpose 
of classification is to place each pixel in a satellite image in a specific land cover or 
information class through the use of the spectral pattern of the pixel (i.e. only within 
the same image would trees and desert each have a different spectral pattern). At a very 
basic level, it is a way of organizing the satellite image into specific clusters of similar 
pixel groups. The classification does not have to be based solely on the spectral pattern. 
It could include spatial or temporal patterns in combination with the spectral pattern, 
or it could simply stand on its own. Only ground-truthing can reveal which classes have 
been classified correctly, since classification is only as good as the information one has 
about the region (Jensen 1996: 247). 

In a supervised classification (Figure 4.8) one can identify the subsets of information 
classes composing the entire image. One can first determine the spectral signature 
of each information class (e.g. lake, forest, wetland, and desert) in what is called the 
"training stage." Training stage sites have a specific value due to the homogeneity of 
the area from which they were selected, and by selecting training regions, classes of 
data can be separated from one another (Jensen 1996: 205). The person running the 
classification creates polygons that enclose whatever specific areas they wish assessed 
(e.g. lake, forest, wetland, or desert areas). Once the user has outlined a number of 
such regions (the number of regions, placed in polygons, is determined by the user), 

Figure 4.8 Manuas, Brazil, X-SAR, supervised classification, image courtesy of NASA. 



the spectral properties of each pixel in the entire image are compared to the spectral 
signature of each information class. Every pixel from the image is then assigned to 
an information class based on the closest matches. Within the image data, one has 
to outline the areas of homogeneous cover and ignore the relative variance in specific 
signature classes (i.e. all high vegetation cover would be classified under "trees"). 

If training data is selected from specific bands, then the formulae >10 n pixels of 
training data must be selected, where n equals number of bands. For example, in the 
selection of three bands in a Landsat image, more than 30 (or 10 x 3 bands(n)) pixels 
must be contained within a polygon of any landcover class. If this number of pixels is 
not obtainable due to insufficient area coverage, the classification will not work (pers. 
comm., Gregoire 2001). Many classification results may be ambiguous due to this reason 
(Wilkinson 2003). A GPS instrument can be used to increase the accuracy of using pixels 
less than 30 x 30 m, but only after ground-truthing has been conducted. This may not 
be feasible in the initial stages of an archaeological project. 

Unsupervised classification is different from supervised classification in that the com- 
puter automatically defines each region, while the user either specifies only the number 
of classes to display, or allows the computer to run pre-programmed "passes" through 
the data and decides when to stop (e.g. at 5 or 10 classes). The computer groups pix- 
els according to statistically determined criteria, seen in Figure 4.9- When using this 
method, one needs reference data and field knowledge, without which the information 
classes can be confusing (Jensen 1996: 197—200). The computer does not know what 
it is classifying and could group classes together that may seem similar from a spectral 

Figure 4.9 Lake Peten Itza, Guatemala, 4-3-2 Landsat unsupervised classification (scale 
1:19400 m), image courtesy of NASA. 



perspective but in reality may not belong together (for example, different types of crops). 
Someone conducting a classification will have a better idea about how to group pixel 
classes together and will be able to make informed choices based on maps or previous 
experience visiting the area in question. 

Classification remains the primary tool of analysis for most archaeological projects 
using remote sensing, as the ultimate goal of most remote sensing projects is to iden- 
tify the specific spectral signature of ancient archaeological sites and features, and to 
distinguish them from the rest of the satellite image. Examples of projects using clas- 
sification include work in England applying a Landsat MSS in the Stonehenge region 
(Fowler 1994c) and in Greece, where a Landsat TM image and classification methods 
showed the extension of fans and faults, not otherwise seen on maps (Barasino and Helly 
1985; Helly et al. 1992). In the USA, one project used electromagnetic induction, sur- 
veying, aerial photography, and a Landsat image in creating a spectral class signature, 
and then applied the information in creating a supervised classification. This showed 
undocumented quarry areas, with a 20 percent success rate for finding new sites in 
comparison to a 0.001 percent success rate for randomly achieving results (Carr and 
Turner 1996). 

Since classification is a method that groups regions sharing spectral characteristics, it 
can help to locate areas with similar characteristics, including environmental settings 
for site placement. One project used a supervised classification along the Delaware 
coastal plain in order to relate their findings to environmental variables (Custer et al. 
1986) such as soil or vegetation types. Classification in the Arroux River Valley region 
of Burgundy, France, aided in the creation of land cover use maps, incorporated into 
a GIS for the Celtic Iron Age (Madry and Crumley 1990). Detection of specific soil 
types can aid in archaeological site detection, or can detect pollutants in the soil that 
may affect archaeological sites (Goossens and Van Ranst 1998). Image classification has 
helped archaeologists to understand broad landscapes in Central America (Sever and 
Wagner 1991; Sheets and Sever 1991), the Middle East (Donoghue et al. 2002), and 
Europe (Cooper et al. 1991; Fowler 1994b, 1995b; Montufu 1997; Bailey et al. 1998). 
In Egypt, running a supervised classification to detect previously unknown ancient 
sites in the Delta region did not work in differentiating modern sites from ancient 
settlement, while it did work in Middle Egypt (Parcak 2007b). This proves that the 
same remote sensing techniques may work or not work in different regions of the 
same country. 

Classification will allow for the identification of spectral signatures of either entire 
archaeological sites or vegetation types associated with ancient sites. If anomalies appear 
during satellite imagery analysis of an archaeological landscape, one cannot immediately 
assume that they represent ancient remains. One of the training areas, if possible, should 
be a known archaeological site or a large feature (if dealing with coarser resolution 
imagery). Once classified, a spectral signature can be associated with that feature or site. 
One might assume that other parts of the classified satellite image that have the same 
spectral signature (shown by the same color in the classification) would be the same site 
or feature. If in a poorly surveyed area, this could only be confirmed through ground- 
truthing. The classification would help the team to zoom in on specific areas to visit. 
Not all classified data with the same spectral signature of the ancient remains will turn 
out to be sites or features. Using data collected during ground survey will help refine 
the remote sensing analyses, as other types of classification may need to be attempted. 



Figure 4.10 The Great Wall of China, showing reconstructed and unreconstructed sections 
(scale 1:70 m) the arrow points to an unreconstructed section, 2008 image courtesy 
of Google Earth™ Pro. 


Image thresholding is a form of classification, whereby the user can specify what parts of 
image values are to remain visible, and what parts to obscure. For example, if something 
has a certain NDVI value, the user can tell the computer that they only want vegetation 
within a certain NDVI range to remain visible. One archaeological team used this 
technique to identify vegetation patches associated with ancient wells and subterranean 
streams along the West coast of Sinai, Egypt. Pharaonic expeditions would go to Sinai to 
obtain copper and turquoise, and would need these water sources for their seasonal camps 
(Mumford and Parcak 2002). Using the "Beijing No. 1" satellite, launched in 2001 
with a 32 m resolution, archaeologists used thresholding to locate largely non-collapsed 
sections of the Great Wall of China (Yuqing et al. 2004) (Figure 4.10). 

Principal Components Analysis 

Principal Components Analysis (PCA) is another form of imagery classification. PCA 
reduces the amount of spectral redundancy in remotely sensed data. With seven bands of 
information being recorded in Landsat imagery, there can often be inter-band correlation 
and information from the digital data gets repeated (Lillesand et al. 2004: 518—19). 

Since this repetition can obscure data, running a PCA can help to clarify classifications 
or band combinations (Figures 4.11a and 4.11b). In calculating the principal compo- 
nents, there will be much greater variance with first principal component compared 
with the second, third, and others. All of the information is compressed into "x" new 



Figure 4.11 North Croatia: (a) Landsat band 1 PCA; (b) Landsat bands 1-4-7 PCA (scale 
1:40000 m), images courtesy of NASA. 

bands, known as the components. It essentially is another method of classification 
through which variables can be classified. With PCA, the first principal compo- 
nent (PCI) contains the greatest variance with the satellite image. Each subsequent 
principal component has less and less of the image variance (Lillesand et al. 2004: 
536—42). There is a maximum of six components, each representing a band in a Land- 
sat image (minus the thermal band). Other satellite images with more or less bands 
(such as ASTER or Quickbird) will have more or less components depending on band 

PCA is an advanced remote sensing analytical tool, and one that several archaeological 
projects have used to make otherwise invisible features and sites appear. The region of the 
Western Karakum Desert in Central Asia, around the Amu Darya River, once formed 
part of the Achaemenid Empire, from the 7th— 5th centuries bc. This river changed 
its course often, thus making locating ancient sites difficult. Predictive modeling was 
attempted using Landsat 5 imagery and PCA. The second principal component detected 
an eastward migration of the river, thus aiding future site detection (Gore 2004). PCA 
has also helped teams to detect archaeological sites in the Mississippi River Valley and 
buried walls in Russia (Stafford et al. 1992; Garbuzow 2003a; Garbuzow 2003b). In 
Tuscany, PCA located many archaeological anomalies on IKONOS imagery first noted 
in aerial photographs (Campana 2002). 

One of the most detailed uses of PCA took place during a remote survey of the area 
surrounding the sites of Ninevah and Ashur in northern Iraq. This study combined 
negatives from Corona imagery georeferenced to SPOT data and corrected L2 ASTER 
data. The data aided the study in identifying both human and naturally created paths, 
yet the Corona imagery did not do as well identifying the "hollow ways" as the other 
imagery. As ASTER is sensitive to many different types of moisture and IR, the study 
used a PCA on bands 1—3. These bands are particularly sensitive to plants and soils. 
It was necessary for the study to examine the reflectance curves of different types of 
minerals and materials (material culture remains such as pottery and other items with 
high organic content). After variance was identified in the imagery, the SWIR (30 m) data 



was merged with the VNIR. PCA identified many different types of soils, vegetation, 
and minerals. In another study area west of Mosul, PCA was applied to bands 2—4 and 
identified hollow ways and ancient canals. Due to the dark brown moist clays in an 
arid soil area, PCA identified an Islamic period canal. Overall, the study emphasized 
matching the wavelength of reflectance data of unknown features with known data 
(Altaweel 2005). 

With advanced remote sensing techniques, a number of factors become important. 
The resolution of ASTER data is not ideal, yet there is much more spectral data when 
compared to Quickbird imagery. Even though archaeologists could attempt to merge 
Quickbird and ASTER data, the 15 m resolution is still too broad (625 Quickbird 
pixels fit into one ASTER pixel). Corona imagery may identify older landscape features 
now lost, but there is no way to make Corona imagery multispectral. Archaeologists 
need to consider the best possible combinations of imagery data when planning to 
run PC As. Some image band combinations may be better than others; the only way 
to know is to test different combinations on a variety of images. Buried features will 
appear depending on the season and the vegetation surrounding them. Archaeologists 
already know that past sites tend to have higher concentration of phosphorus, which 
is another chemical to search for using PCA. NASA's Jet Propulsion Laboratory has 
a library of reflectance data that can be used to compare pixel values in satellite 
imagery (see http:// This library is by 
no means complete, and there may be confusion arising from pixels containing mixed 
data. Archaeologists should also have a good understanding of the soils and geology 
of the regions in which they are working. PCA is an excellent method to attempt 
when using multispectral imagery with a number of bands, and where spectral mixing 
is likely. 

Land Use Land Cover Changes 

Comparing Land Use Land Cover Changes (LULC) over time is a remote sensing tech- 
nique with much to offer archaeologists (Figures 4.12a and 4.12b). After running 
classifications on satellite data from two or three periods, archaeologists can begin to 
assess overall landscape changes. This is a technique that works best with a smaller num- 
ber of landscape classes. Once the spectral signatures of archaeological sites have been 
identified, perhaps the study can reclassify the data into groups such as "archaeological 
site," "urban," "vegetation," and "soils." Using remote sensing programs, the user can 
specify colors for pixels that change between each dataset and pixels that do not change. 
For example, pixels may show "vegetation to urban," "archaeological site to vegeta- 
tion," or "vegetation (unchanged)." This technique can show how much landscapes have 
changed over time, or can show how much of an archaeological site remains after destruc- 
tion from natural or anthropomorphic factors. In a rich archaeological landscape in the 
Crimea, where there are 140 known Greek and Roman archaeological sites, Landsat 
imagery from 1972—2000, IKONOS imagery, and Corona high-resolution space pho- 
tography showed that urban encroachment was affecting archaeological sites (Trelogan 
and Carter 2002). Comparing 2002 Landsat imagery with 1972 Corona images in Mid- 
dle Egypt showed a 200 percent increase in urbanization, which affected the preservation 
of many multi-period sites (Parcak 2003, 2004c, 2007a). Assessing how LULC might 
affect conservation issues (discussed in Chapter 8) is a good approach. 



Figure 4.12 Mansourah, Egypt, Land use land cover change showing urbanization, compare: 
(a) 1972 Corona image; with (b) 2008 Google Earth™ Pro image (scale 1:4130 m), 
especially the extensive southern growth, images courtesy of US Geological Service 
and Google Earth™ Pro. 

DEM techniques 

SRTM data, discussed in Chapter 3, allows archaeologists to model landscapes in 3D 
(Forte 2000; Hagerman and Bennett 2000) (Figure 4.13). Techniques for turning flat 
data into 3D vary per remote sensing or GIS program. The coarser the satellite data, the 
easier it will be to drape it over SRTM data to create a DEM. DEM data can be extracted 
from either SRTM data, stereo-pair SPOT data, ASTER data, or from stereo Corona 
images. Archaeological projects have used SRTM data to create 3D landscape models in 
India (Golchale and Bapat 2006), Ecuador (Yugsi et al. 2006), and Mongolia (Kumatsu 
et al. 2006). Broader uses include creating DEMs for paleohydrology (Piovan et al. 2006) 
to detect river changes over time in Italy (Ferrarese et al. 2006). In Languedoc, France, 
SPOT, Landsat, ASTER, and SAR data allowed the reconstruction of a Bronze Age 
landscape with 350 sites. The edge of the sea and previous river channels were detected 
and the DEM allowed the team to compare the locations and dates of the known sites 
with the old coastline (Ostir and Nuninger 2006). In a study of the Wari Empire of 
Peru, archaeologists used DEMs to show relationships between 20 administrative centers 
found in 12 valleys across modern Peru as well as the general landscape topography. 
They used this data to predict correctly additional ancient site placement (Jennings and 
Craig 2003). 

Landscapes change in different ways across the globe. DEMs show change at varying 
scales if archaeologists use the topography as a form of proto-architecture. DEMs can be 
combined with satellite imagery to create detailed basemaps, which can then be imported 
into a GIS. Viewshed analyses show the locations of river courses and other water sources, 
which archaeologists can use to predict archaeological site locations. Landscape models 
can show migration or diffusion paths, and locate the most likely places for settlement 
(Evans and Farr 2007). On a generally flat terrain, past peoples were more likely to 
settle on a raised area to give them a defensive position in times of conflict. Each culture 



Figure 4.13 DEM of Machu Picchu, Peru, SRTM, (Scale 1:202 m)2008 image courtesy of Google 
Earth™ Pro. 

will have varying relationships with their landscapes. Understanding how past peoples 
interacted with their topographies allows better archaeological analyses to take place. 

Hyperspectral studies 

Hyperspectral satellites, with large numbers of bands, allows for more targeted analyses. 
If the investigator knows the spectral signatures of the features in question, especially 
if they are built from a specific type of stone, or have a certain type of vegetation 
growing atop them, then they can do specialized classifications, PC As or other analytical 
techniques. One study detected shell mounds in the Jiangsu province of China using 
128 bands of data from an Optical Monitor Imaging Spectrometer (OMIS) sensor with a 
resolution of 6.6 m. Shells are composed of calcium carbonite and have a strong spectral 
absorption between 2000—2550 nm. The analysis detected the XiXi shell mound (see 
Figure 4.14) measuring 15 x 100 m, with associate material culture remains of jade, 
stone, pottery, and bronze. Results were tested in the field with a spectrometer, which 
found that the shell had a higher spectral response than the surrounding soils (Qingjiu 
and Jianqiu 2004). Another study using an OMIS sensor noted that values can vary, and 
that resampling is needed to correct the images. Atmospheric interference can affect the 
imagery readings (Xiaohu 2004). 

If the groundcover is mixed, results from hyperspectral analyses can be problem- 
atic. When vegetation cover is less than 30 percent an AVIRIS senser (Airborne 
Visible/Infrared Imaging Spectrometer) will not be reliable. With low spectral con- 
trasts, vegetation types cannot be modeled correctly even where there is high density 



Figure 4.14 Shell Mound, Cagayun River, Philippines (scale 1 :80 m), used as a comparative shell 
mound image from space, the shell mounds are numerous and dot the entire image, 
2008 image courtesy of Google Earth™ Pro. 

vegetation cover (Okin et al. 2001). This shows how important it is for archaeologists 
to have a holistic approach to landscapes. Using a MI VIS (Multispectral IR and Visible 
Imaging Spectrometer) sensor with 102 bands at the archaeological site of Aquila, Italy, 
archaeologists revealed how problems could result from hyperspectral remote sensing. 
Anomalies were often improperly detected (Traviglia 2006). Just because linear features 
appear in an image does not mean that archaeologists should equate them with archae- 
ological features. Caution remains the best approach with satellite archaeology, even 
when using the most advanced sensors and most advanced remote sensing techniques. 


Spatial filtering is a common remote sensing method used to highlight differences in 
tonal image variations, and has many applications for archaeological contexts. Subtle 
variations between neighboring pixels can be emphasized or de-emphasized depending 
on the filters applied (Figure 4.15). This is done via the image process of convolution, 
where kernels or coefficients can be applied to weight the digital numbers. For example, 
in a 5 x 5 kernel with coefficients equal to 1/2 5th, all of the kernel's original digital 
numbers would be multiplied by 1/2 5th, added together, and divided by 25 to get a 
central pixel value. Filtering can remove image noise, or can stress differences in edge 



Figure 4.15 Auburn University, Alabama, Quickbird low pass filter (scale 1 : 100 m), 2008 image 
courtesy of Google Earth™ Pro. 

values in either low or high frequency change areas (i.e. desert dunes versus a large city) 
(Lillesand et al. 2004: 517—31). Different remote sensing programs will offer varying 
filtering options, which may include curvature, digital elevation models, Gaussian, high 
pass, low pass, standard, sun angle, or user code, under the algorithm load filter. Each 
listed filter may then have a subset of many additional filter types. Filters can be applied 
before or after specific remote sensing formulae, or the user can apply more than one 
filter for advanced applications or code in C. 

Edge detection will generally be the most useful filter archaeologists can apply in 
their work, yet testing the full range of filter types is important. Like all remote sensing 
techniques, the filters that may work best depend on local conditions and satellite 
image types. Not all studies using high-resolution data will apply filters (Richetti 
2004), yet each study is case-specific. One example from the Peten in Central America 
used Landsat TM, IKONOS, and SAR data to examine relationships between landform 
types, vegetation and settlements using a 3 x 3 filter (Estrada-Belli and Koch 2007). 
Using CASI and LIDAR data in Greece at the archaeological site of Itanos, one team 
found a garrison wall and additional stone walls with edge detection (Rowlands et al. 
2006). Another study at the site of Hieropolis, Turkey, used a combination of high- 
resolution SPOT imagery and edge detection to identify faults affecting the site (Leucci 
et al. 2002). The archaeological site of Xucutaco-Hueitapalan in Mexico was mapped 
using ERS-2 and SAR data over an area measuring 3x3.5 km. Archaeologists compared 
filtered versus unfiltered data, and found the filtered data revealed a previously unknown 
complex of structures (Yakam-Simen et al. 1997). 



Figure 4.16 Mt. Ruapehu volcano, New Zealand (scale 1:531 m), image courtesy of NASA. 

RADAR/LIDAR analysis techniques 

SAR and LIDAR data can be difficult to process, and the techniques one can apply 
depend on the quality of the imagery. SAR imagery cannot be seen the same way as 
multispectral imagery due to imagery distortions, oblique viewing geometry, and the 
way the microwave sensors work. Although SAR imagery is an "all weather" imaging 
system, there can be problems associated with mapping landscapes with varying types of 
relief. Additional landscape distortions may appear with regions that have higher reliefs, 
such as mountainous landscapes (Gatsis et al. 2001). When landscapes have homogenous 
and fine-grained surfaces, RADAR data can help with identifying subsurface features 
(Figure 4.16). In the Sahara, where it rains once every 20—50 years, a scientific team 
examined how wind shifted sand over time in Egypt's western desert using SIR-C and 
RADARS AT data. With less than 1 percent surface moisture content, this area proved 
to be an ideal testing area for RADAR imagery: Surface moisture can increase image 
backscatter. The team identified two major river courses as well as inland basins (El-Baz 
et al. 2007). Combining SAR data with multispectral data is generally the best way to 
approach archaeological site discovery. One archaeological team used a combination of 
SAR, SPOT, Landsat, and SIR-C data to aid in identifying three missing pyramids in 
a survey south of Cairo, Egypt from Abu Rawash to Dashur. SPOT and Landsat data 
allowed the team to search for artificial shapes, and they then found four sites in the 
area of Dashur North. Polarizing the L-band from the SAR data SIR-C DATA made it 
possible for the team to locate two previously unknown sites. With excavation, these 
proved to be two tomb shafts (Etaya et al. 2000). 



One of the most focused studies using SAR data took place on San Clemente Island, 
California. The team used SAR data to develop protocols for archaeological inventory 
surveying Department of Defense (DoD) land and assessed both archaeological and envi- 
ronmental factors. SAR data collects physical rather than chemical aspects of landscapes, 
so prior site knowledge is important. Surveying DoD and Federal lands for archaeolog- 
ical materials consumes US$19 million a year, and if protocols can be developed using 
satellite imagery (being a non-destructive and non-invasive technology), up to 30% of 
project costs can be saved. The SAR data was flown on a DC-8 jet with three bands 
(P, L, and C, with resolutions ranging from 1.8—7.5 m). The L and C bands worked 
best for archaeological site identification. Overall, the SAR data identified 701 sites. 
Sites were associated with slope and aspect, soil drainage patterns, and water collection 
areas. Of identified sites 95 percent faced west, towards the wind, in the northernmost 
survey area. This was likely the most advantageous area for food and a defensive lookout 
position (Comer and Blom 2007). Site positions in landscapes can reveal a great deal 
about landscape meaning, while thinking about advantageous site positioning can aid 
archaeologists when using slope, aspect, and other landscape-specific techniques to assess 
SAR data. 

Similar issues need to be considered when using LIDAR data (Figure 4. 17). Backseat - 
tering from the target area depends on its overall size distance from the aircraft, overall 
reflectivity, and the direction of the scattering. In a study using LIDAR for archaeological 
detection in Leitha, Austria, a number of techniques were attempted to locate features 
in dense forests. The project stressed the importance of testing a number of methods, 

Figure 4.17 New Orleans, LIDAR imagery (scale 1:827 m), note how LIDAR makes the roads 
and canals much clearer, image courtesy of NASA. 



as previous studies had distinguished trees from the ground but not vegetation from 
the ground. Vegetation comes in different heights, and is distributed unevenly across 
the terrain. LIDAR laser sensors can read between two and four echoes, with individual 
point scatters being distinguished. As a result, location scattering properties of single 
targets can be read and given coordinates. Each type of forest canopy will require the user 
to choose optimal sensor characteristics, and will require special tuning so archaeological 
features will not be missed. Thresholding assisted the team with eliminating points off 
the terrain from the analysis. Overall, the project identified ramparts and a graveyard 
with 50 barrows (not visible in aerial photographs). The study represents one of the 
first times LIDAR was used to detect archaeological remains in forested and vegetated 
areas, which required a far more accurate landscape analysis (Doneus and Briese 2006a, 
2006b). A similar study in the Midlands and in York, UK used LIDAR data to examine 
different returns from the lasers. The study determined that a fall-off in light inten- 
sity can equate with river paleochannels. The first pulse return showed forest canopy, 
the second return showed bushes, and the third return showed features on the ground 
(Challis 2006). 

GIS and remote sensing 

GIS and satellite remote sensing are outwardly even more closely related than subsur- 
face surveying techniques and satellite remote sensing They are often confused, but 
together can be used to bridge the gap between remote sensing specialists and archaeol- 
ogists (Castleford 1992). Both offer ways of visualizing and organizing remote sensing 
and additional data for archaeological discovery and predictive modeling, and do so in 
different yet related ways (Stine and Decker 1990; Johnson 1996; Forte 1998; Limp 
2000; Forte 2002; Rabeil et al. 2002; Som 2002; Gupta et al. 2004; Rajamanickam 
et al. 2004; Belvedere et al. 2006). How GIS and remote sensing can be combined to 
further archaeological analysis is discussed here. A GIS is a detailed data visualization 
system, and allows the input of multiple layers of data to view spatially view and orga- 
nize statistical information. Within a GIS, layers can be created, turned on, or turned 
off, for comparative and analytical purposes. Shapefiles can be created to highlight spe- 
cific data, and data can be added to specific points or shapefiles. Users have the ability 
to observe patterns between the layers of data (Figure 4.18). Statistical data can also 
be compared in a GIS, which may aid in archaeological predictive models (Symanzik 
etal. 2000). 

Ultimately, a GIS is only as good as imputed or imported data. While a GIS allows 
viewing of satellite imagery, it cannot run the same processing algorithms as a specific 
remote sensing program. Satellite remote sensing programs allow GIS layers to be added 
as well as the creation of shapefiles that can then be exported to a GIS. With satellite 
remote sensing, the analysis is only as good as the understanding of the processes behind 
the imagery analysis. The remote sensing user needs to be able to make choices regarding 
type of imagery and processing techniques. Both GIS and satellite remote sensing work 
require previous training, and an understanding of the pitfalls of each method of analysis. 
Many courses, in fact, combine training in satellite remote sensing and GIS. Satellite 
remote sensing is, at its core, how radiation reflected off the Earth's surface is captured 
and interpreted, while GIS is about evaluating, analyzing, comparing, and contrasting 
layers of imported data. Together, satellite remote sensing and GIS are effective tools for 



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Figure 4.18 Tannehill, Alabama, GIS map showing digital elevation model (scale 1:3200 m), 
image courtesy of NASA. 

archaeological analysis, and discussing the best ways they can be combined will allow 
archaeological projects to make the best of each tool. 

Correlating known and unknown data is another useful feature of integrating GIS 
and remotely sensed data. There may be additional patterns that remote sensers cannot 
see on the satellite imagery alone. This includes examining regional archaeological site 
orientation (if the satellite imagery used is too coarse when identifying "new" features) 
and looking at site-specific data. If 80 percent of known archaeological sites in an area 
have a hilltop temple nearby, then any sites found in a regional survey will likely have 
an 80 percent chance of having this similar feature. Examining known site distribution 
and associated features in a GIS will aid in regional surveys. Perhaps the remote sensing 
analysis only had a 50 percent success rate for identifying known archaeological sites in 
an area. A GIS may help remote sensers understand why the other 50 percent of sites 
were not seen. In a GIS, data can be entered on a site's date, features, soil types, and any 
other features the user may deem important. Turning on and off layers that display those 
features in relationship to the remotely identified known sites may aid archaeologists in 
attempting additional methods of analysis. 

Once cultural resource managers have examined multiple phases of data in a GIS, 
they can spot varying trends, and create site management plans. In a study of the 
flooding surrounding the Usumacinta River in Mexico and Guatemala, the project used 
Landsat and SRTM imagery to locate 18 previously unknown ancient sites. This data 
was imported into a GIS to show the flooding risk to known Mayan sites as well as the 
recently identified archaeological remains (Beredes, as cited in Stubbs and McKees 2007). 



In Khirbet Iskander, Jordan, archaeologists produced a DEM to show an archaeological 
site drainage plan at varying levels of elevation across the site. Placing this data in a GIS 
aided cultural resource managers in identifying flood impact zones (Peterman 1992). 
Urbanization, a cultural resource problem surrounding many archaeological sites, can 
also be addressed through using a GIS. One project looked at old maps and modern 
high-resolution satellite imagery in Cairo to map the change from rural to urban areas 
through time (Stewart 2001). Using a combination of old maps, aerial photographs, and 
satellite images will give the widest range for comparative purposes. 

Archaeological sites can never be thought of in a mere "there or not there" context 
when combining satellite imagery and GIS. Sites need to be considered from the perspec- 
tive of their accessibility, visibility, and defensibility. In a modeling project in the region 
of Pinchango Alto, Peru, archaeologists carried out spatial modeling using orthophotos, 
terrestrial laser scanning, and 270 million differential GPS points, and put everything 
together in a GIS to understand structure and site placement through time (Lambers 
etal. 2007). Through a GIS, archaeological projects can also combine space- and ground- 
based remote sensing tools. One project in Metaponto, Italy, placed digital imagery and 
magnetic data in a GIS for comparative purposes (Riccetti 2001). Spatial organization 
of specific sites is another factor which can be assessed using satellite data in a GIS: 
Newly located features on a site can be compared with excavated or ground-sensed data 
(Williams etal 2007). 

A GIS can allow layering of information to assess landscape changes and the plot- 
ting of archaeological site placement through time, especially in relationship to needed 
natural resources. This can be helpful in riverine regions where ancient sites change 
location based on access to water (Timor 2004). In Russia, one study examined forestry 
resources and subsistence practices during the Bronze Age. After establishing paly- 
nological sequences, the study compared pollen analyses to vegetation zone mapping 
using Landsat TM, ASTER imagery, and NDVI. All the data was imported into a 
GIS to assess overall changes, and both scale and general patterns were deemed diffi- 
cult to see (Vicent et al 2006). One can also use a GIS to compare analyzed satellite 
imagery with soils that may have similar geophysical characteristics. A study examining 
data from Ureturituri Pa and Fort Resolution, New Zealand used a GIS to charac- 
terize remotely sensed data and identify previously unknown archaeological remains 
(Ladefoged et al 1995). 

Integrating remote sensing and GIS allows for a full range of archaeological assessment 
activities, especially protecting past sites. Ancient sites do not move by themselves, and 
a combination of past and future data is needed to protect cultural resources. One 
study claims that few papers have gone beyond using GIS for data collection. The 
authors advocate four main uses for GIS in archaeological research: Studying the spatial 
structure of modern agricultural landscape, assessing ground visibility of archaeological 
remains, defining and characterizing sites, and, looking at decision making behind 
archaeological site location (Bevan and Conolly 2002). Finding spectral signatures in 
a landscape is only the first step: How should archaeologists work with government 
organizations to protect cultural landscapes? Many major sites are tourist destinations, 
but their hinterlands should be protected as well. Governments do not always realize 
the impact of modern building on preserving the past. For example, roads in northern 
Thailand were built through temple complexes (Stubbs and McKees 2007), yet this and 
other similar situations may be more closely connected to issues of corruption. Managing 



past sites requires a full understanding of past and present landscape processes, and this 
can be best understood through satellite remote sensing analysis, which can detect most 
forms of encroachment, whether through urbanization or deforestation. 

What are the advantages and disadvantages for using remote sensing in combination 
with a GIS? Remote sensing programs do not cope well with large imported databases, 
which are always necessary when carrying out ground survey. ArcGIS® has imagery 
extension programs that allow users to import a number of image types. Archaeologists 
can take analyzed imagery and link specific sites in a GIS with data they collect during 
their ground survey seasons. Broader landscape changes can be quantified, especially 
when a land use land cover change study is conducted. Regional dynamics can be better 
understood in a GIS, as archaeologists can layer dozens of different data types. Predictive 
modeling is another area now beginning to be explored with satellite archaeology. Data 
in a GIS can help narrow down possible archaeological site locations even further, as old 
maps may show previous landscape features that the remote sensing analysis did not 
detect. Importing archaeological satellite data into a GIS will give the broadest range 
of results to a remote sensing project (Limp 2000). 

Combined techniques 

Most archaeological remote sensing projects attempt more than one analytical approach. 
How teams or individuals choose to combine approaches gives insights into how people 
should model their own remote sensing projects for archaeological exploration. Each 
region will present specific problems or issues that only multiple algorithms can assess. 
No archaeological project will be able to predict which techniques will work best in 
feature identification. How to approach specific landscape and site types and design 
remote sensing analyses accordingly is discussed in great detail in Chapter 5. Here, 
select projects will be discussed for the overall remote sensing problems they faced, and 
how they addressed specific challenges with multiple types of inquiries. 

In regions where no remote sensing for archaeological work has been conducted, 
teams may need to adopt a more geological approach. This was the case in many early 
remote sensing studies in archaeology. In Libya, a team used Landsat MSS imagery to 
discriminate landscape types using maximum likelihood classification, principal com- 
ponents analysis, and smoothing with a 3 x 3 filter. The landscape classification revealed 
limestone plateaus, basalt formations, alluvial basins, and different types of cultivation. 
This analysis aided in a paleoenvironmental survey (Dorsett et al. 1984). 

Finding archaeological sites in areas with alluvial deposits presents a great challenge to 
archaeologists. One does not know if the alluvial deposits will have covered over ancient 
remains fully or partially, and if the area still has a great deal of moisture in the soil, it 
may affect the techniques that remote sensers can apply. In a project detecting sites in 
the Yellow River region of China, dating to the early Shang Dynasty, archaeologists used 
Landsat 5, Landsat 7, SPOT, Corona, and SAR data. Seasonal moisture changes affected 
the methods archaeologists could use to detect buried rectilinear features, detected 
largely from the SAR data (Blom et al. 2000). Landsat TM imagery also contributed to 
a study mapping prehistoric Hohokam culture features (ca. 300 BC— AD 1450) along the 
Salt River in Arizona. The culture used canal irrigation to grow corn, cotton, squash, and 
beans. As known canals measured between 200—600 km long and 26 km wide, it was 
assumed that remote sensing analysis would detect additional canals. The study used 



contrast stretching, spatial filtering, and band combinations, as well as a subsequent 
ground check, to detect a potential canal fragment, which proved problematic given 
that the imagery used was taken in July. Future work using imagery from a wetter time 
of year would likely reveal additional canals. A number of faint linear features appeared, 
but they were likely to be modern (Showater 1993). 

Using ASTER IB imagery, SPOT 5, IKONOS 2, and Corona imagery, one project 
at the site of Hisar in Turkey compared how imagery affected the general visibility of 
archaeological remains. The project noted that Landsat ETM+ imagery did not have the 
resolution to detect smaller features, with IKONOS, Quickbird, Corona imagery, aerial 
hyperspectral imagery, and aerial photography having the highest resolution available. 
Numerous studies, however, have made use of far coarser resolution imagery to detect 
sites. Maximum likelihood classification, segmentation, nearest neighborhood classifica- 
tion, and filtering were the methods chosen by the team for the remote sensing analysis. 
Archaeological features did not appear in the classes in which the team expected them to 
appear, and the filtering did not detect exposed walls. Perhaps the imagery did not date 
to the appropriate time of year, or atmospheric conditions strongly affected the imagery 
(DeLaete/*/. 2007). 

Towards automated archaeological site detection? 

Automated archaeological site detection would be an ideal outcome for archaeologists. 
Using known algorithms, archaeologists could enter their imagery data in remote sens- 
ing programs, and click "locate feature X." The computer program would run through a 
list of algorithms based on the imagery being analyzed, and would ask questions regard- 
ing archaeological site types in the area, ground conditions, and any other pertinent 
information. Unfortunately, this is not possible, based on the very tenets of archaeology. 
Archaeologists excavate sites following strict scientific methods, while local conditions, 
rules, and general support will influence the methods chosen to excavate those sites. 
Perhaps a team is more interested in excavating graves to study paleopathological issues, 
or is interested in comparing industrial production techniques across a region. Satellite 
archaeology is the same: Every project will be different, with each project director 
determining the direction the work will take. A team may be more interested in envi- 
ronmental issues, locating past river courses, or may be interested in locating features on 
sites to excavate. Each project drives the remote sensing techniques that can be applied, 
and ultimately, the survey design. 

Computers simply do not have the same ability as human eyes have to pick out sub- 
tleties in remotely sensed images. Only the viewer will know what he or she is looking 
for, based on their background and understanding of the archaeological situation. One 
cannot input the thousands of minor variables into computers that influence archaeolo- 
gists when making choices about archaeological data. How will a computer be able to 
assess similar broad issues for ground surveying? As archaeologists, we can make choices 
regarding what information we want displayed on satellite imagery, and how we use that 
information to plan survey seasons. Computers cannot tell if a site or feature is present 
or not; they just facilitate the display of pixels. It is up to us to determine what those 
pixels mean. 

Thus, there is no "one size fits all" for remote sensing techniques in archaeology, and 
there can never be any "automatic" feature extraction in archaeological remote sensing. 



Archaeologists must assess project needs on local and regional levels, and attempt to find, 
specific remote sensing algorithms that best fit project goals. The importance of knowing 
the archaeology and related architecture of a region cannot be understated: Depending on 
the type and size of structures in question, archaeologists can apply alternate analytical 
methods to different imagery. Why does there even need to be an automated process for 
satellite archaeology? One would not want the process of archaeological discovery to be 
automated on the ground, and the same would apply for locating sites and features from 
space. Certainly, more mathematical models are needed for satellite archaeology (Li et al. 
2004), yet all projects will be affected by data quality. Ultimately, the question should 
not be if there is any "automatic" way to locate sites, but how archaeologists can approach 
individual remote sensing projects in a scientific way, with a good understanding of what 
remote sensing analytical methods can offer their work, and what they cannot. 




How we interpret the visual world is a combination of perception and comprehension. 
Perception may differ from person to person, yet the scientific laws by which the Earth 
is observed are immutable. It is the crossroads of perception and comprehension where 
we can place the field of remote sensing. While the science behind remote sensing is 
fairly well understood, how researchers can interpret the meaning behind what they see 
is not. Everyone's eyes remotely sense through their own unique "lens" that provides 
each person with a completely different perception of the world surrounding them. 
Transforming alternative perceptions into a common archaeological approach would at 
the outset seem unlikely. What one archaeologist sees as a stable, another would see 
as a storehouse (Holladay 1986). There are countless additional examples of similar 
archaeological debates. If archaeologists cannot agree on defining the meaning of basic 
structures, can they adopt similar approaches for characterizing entire past landscapes for 
feature location from space? Every landscape is different, depending on weather, climatic 
conditions, population density, seasonality, and innumerable additional environmental 
and anthropogenic factors. Similar things affect satellite imagery (Hirata et al. 2001). 

Approaching landscapes systematically from space mean any remote senser should 
have a broad archaeological understanding of landscapes. This chapter will provide 
strategies for overcoming these landscape-based problems from the perspective of com- 
pleted and ongoing satellite archaeology projects, and provide alternative suggestions for 
additional approaches archaeologists might have taken. This is not meant as a critique of 
the projects, as the discussion will be based on project successes and will aid other teams 
working in similar places or on related feature detection. When thinking about imple- 
menting a remote sensing project, archaeologists need a starting point. Even if no maps or 
aerial photography exist of the regions archaeologists will be exploring from space, and if 
they have not visited the area before, at the very least they will need to have an idea about 
the landscape types they will encounter, seen in Tables 5 . 1 and 5.2. Once an archaeologist 
has a starting point, it is appropriate to begin to consider the satellites and remote sensing 
techniques needed for various landscape types. Satellite remote sensing techniques will 
be discussed in this chapter for the following categories of landscapes: Rainforest, urban 
(modern), rural, mixed (urban/rural), desert, scrub/grassland, mountainous/hilly, mixed 
landscape, coastal, waterlogged/wetland (including swamps), alluvial plain, tundra, 
frozen/glaciated areas. Although these categories are not exhaustive, they represent the 
majority of landscape types archaeologists will encounter in their work. 



Table 5.1 Landscape types and the most applicable satellite images for archaeological projects 

Aerial Corona Landsat SPOT ASTER Quickbirdl SIR-CI SRTM 

graphy IKONOS X-SAR 











































































Alluvial Plain 



















X X 


X X 


X X 


Table 5.2 Archaeological site types and the most applicable satellite images for archaeological 

Aerial Corona Landsat SPOT ASTER Quickbirdl SIR-CI SRTM 

photography IKONOS X-SAR 


X X 





























































How much of a role virtual landscapes should play in the analysis of these real world 
landscapes remains a topic of debate in archaeology. Archeologists deal with past land- 
scapes undergoing real-time alterations, whose changing features have been illuminated 
by the use of satellite remote sensing. All multispectral satellites, at the level of the 
pixel, display reflected data on computer through digital numbers in false color. This 
is not "true" data in the sense that our human eyes cannot view anything beyond the 
visible part of the electromagnetic spectrum. As such, archaeologists using multispectral 
data create virtual worlds through digital landscapes. This includes draping aerial pho- 
tographs over SRTM or LIDAR data which, even with the 3 cm resolution of LIDAR, 
creates small errors and distortion even with georeferenced aerial photographs. 

Some archaeologists emphasize integrating remote sensing with "traditional" tech- 
niques by creating managed layers of data using virtual reality. Virtual reality does 
have an important place in archaeology, as it allows potential reconstructions of past 



Figure 5. 1 The Acropolis, Athens (scale 1:96 m), note the partial distortion in the image, 2008 
image courtesy of Google Earth™ Pro. 

landscapes according to what each individual archaeologist wishes to emphasize. This 
can lead to an overemphasis on things such as 3D imagery, seen in Figure 5.1. Just 
because it is seen as highly technical should not make something correct or scientifically 
valid (Campana and Forte 2006). Digital landscapes, however, remain in the eye of the 
beholder. Past landscapes have been so altered by innumerable processes and events that, 
with the science currently available, archaeologists cannot make perfect reconstructions 
of them. This must be recognized even as archaeologists use all the science available to 
them to create the best possible imagined ancient landscapes. How this data is communi- 
cated to allow multiple interpretations is crucial, as all remote sensing will be subjective 
based on each user's experience. Instead of a 'Virtual reality," which assumes some type of 
imagination to reconstruct what is not there, why not a "digital reality?" Archaeologists 
see their own interpretations of multispectral imagery based on their backgrounds and 
biases, yet imagery interpretation is quite different than using a computer program to 
reconstruct a largely destroyed temple. It is unlikely that rigorous excavation will reveal 
all a temple's missing pieces, while detailed ground survey allows archaeological teams 
to test their remote sensing hypotheses on ground features initially seen on computer 

A full catalogue of site typologies in archaeology would be invaluable for archaeologi- 
cal survey and remote sensing analysis. Differing site types require the use of appropriate 
satellite remote sensing techniques, and a full range of archaeological sites (ranging from 



sherd scatters to massive urban complex) can be encountered during each ground survey 
season. The process of site recognition from space and during survey should not be 
undervalued: As in excavation, it becomes easy to miss important features by not being 
completely aware of one's surroundings or what is directly underfoot. Just as mud brick 
wall debris may subtly give way to a well-defined wall, gentle sloping of the land might 
obscure an ancient mound mostly covered over by a modern settlement. 

This chapter categorizes archaeological site types into 10 broad categories, and 
describes remote sensing techniques and satellite types that archaeologists can use to 
identify those sites and features. It draws upon many examples, and discusses pitfalls 
to avoid for each category. These landscape categories are by no means comprehensive, 
nor do they suggest that each feature within the landscape type belongs to that cate- 
gory alone. Indeed, nearly every feature type can fit into two or more categories. The 
categories are merely meant to aid remote sensing researchers in devising strategies for 
their remote sensing projects. These categories include: Settlements, linking features, 
water management systems, features temporary and border, installations, agricultural 
features, mortuary features, religious structures, and ceremonial installations. 

Defining an archaeological site is thus not as straightforward as one might assume, and 
begs the question: Is it an archaeological site or feature if it is not visible to human eyes 
or satellite imagery interpretation? What is located 6 or 12 m below the ground under 
a well-defined archaeological site is still considered a part of the site, even though it can 
only be reached through coring or deep trenching. Vertical remains are not questioned 
in terms of whether or not they are part of sites. What about the horizontal remains? 
Just as today, at all points in the past, settlements would have been surrounded by some 
combination of field systems, canals, vegetation, waterways, or roads. These are excavated 
and evaluated in the same manner as archaeological sites. Given the density of human 
settlement and the importance of agricultural land and waterways in the ancient world, 
defining a true "site" is closely connected to ancient landscapes and how the individual 
site remains are interacting with ongoing modern processes. Having a holistic view of 
archaeological landscapes versus specific site locations supports how sites can be explored 
from space, and will guide the suggestions made for their interpretation with satellite 
image types and specific analytical techniques from space. This chapter is also not meant 
as the final word on what will and will not work in each landscape context. It should be 
viewed more as a general guide and starting point, as all landscapes and archaeological 
features will vary in size, shape, and spectral signature. 

Finding meaning in archaeological landscapes will ultimately depend on the back- 
ground and experience of the person responsible for the analysis. All people will view 
a landscape differently, whether one is an artist, geographer, biologist, remote sensing 
specialist, or an archaeologist (Knapp 2000). Each person will bring the tools of their 
craft to the table, along with their biases. As an example, this author would not dare 
to attempt a painting of a scene overlooking the Nile. My artistic skills are limited to 
drawing pottery, sections, and detailed plans with the aid of rulers. Does this mean I am 
any less capable of "seeing" a landscape, or deriving meaning from it, when it is viewed 
from space? Just as an artist will spend hours looking at every detail of a tree or branch 
so they can draw it correctly, I spend hours examining pixels of important features in 
satellite imagery so it can be analyzed correctly. No two archaeologists will see a satellite 
image in the same way, which is why it is important to outline specific landscape and 
features types as a starting point. 



Landscape types 

Each landscape type will have various associated features depending on where they are 
located in the world. Deserts will have higher or lower rainfall levels, rainforests will have 
different types of vegetation, and urban areas will contrast in their form and function. 
It is the shared attributes of each landscape type that complicate archaeological site 
and feature detection from space. Through discussing each remote sensing technique 
and why they worked or will work well for a particular region, archaeologists will be 
able to apply those same techniques on imagery in their own landscape-specific work. 
Some techniques might not apply in a particular region, as discussed in Chapter 4, 
but problems archaeologists might encounter during remote landscape analysis will be 
reviewed. The types of information teams or individuals might need to take into the 
field from the satellite imagery is covered more in Chapter 7 , but is discussed here briefly 
with regards to what might be seen during fieldwork. 

Ultimately, from space, archaeologists are searching for landscape relationships, 
whether through the sense of multispectral data to see how landscapes have changed in 
relationship to other factors, or how past peoples may have related to landscape types 
and features within them. How past peoples may have reacted to global climate events is 
not something that can be addressed alone through remote sensing. However, archaeol- 
ogists always need to take into account the environmental factors that may have affected 
site and feature placement. These past events (including natural disasters) may have 
caused ancient sites and features to be hidden from plain view, yet infrared and thermal 
satellite bands may reveal them on screen. Finding potential water and food sources, 
especially in non-resource-rich environments, would have been primary concerns of past 
peoples. These sources, too, may not be visible unless seen with multispectral data, 
shown in Tables 5.3 and 5.4. Many case studies have shown that identifying resources 
used by past peoples from space will generally lead to archaeological site discovery. 
Archaeologists must look at landscapes from space from a backwards-viewing lens. 
Thinking about how populations survived in these landscapes can aid in formulating 
research strategies and in posing broader questions rather than merely finding additional 


Rainforests present many challenges to archaeologists who wish to use remote sensing 
for archaeological site and feature detection (Hixson 2005) (Figure 5.2). Vegetation 
density makes searching for sites on foot alone virtually impossible. Transportation 
issues abound, as there may be few paths for trucks to follow. Through pinpointing 
exact places to survey, archaeologists can plan surveying strategies to maximize their 
resources. Archaeologists should first examine Landsat ETM+ imagery (with merged 
15 m multispectral data) and ASTER imagery, depending on seasonal constraints. Wet 
season multispectral imagery may not work in detecting vegetation color changes, one 
of the most likely indicators of hidden archaeological sites. Quickbird and IKONOS 
imagery, with a much higher resolution, may aid in detecting more specific areas once 
initial coarser resolution analysis is completed (Saturno et al. 2007). Putting the sites 
into a GIS will aid in areas where little is known, especially for suggesting potential 
routes or trails between major sites (Herrera et al. 2002). 


Table 5.3 Landscape types and the most applicable analytical techniques for archaeological work 

Visual Contrast 






































































































Alluvial Plain 























Table 5.4 Archaeological site types and the most applicable analytical techniques for 
archaeological work 















































































Monumental remains of ancient sites, whether in the rainforests of Central Amer- 
ica, Asia, or Africa, will most likely consist of stone. This material affects vegetation 
through chemical leeching into the soils, which is absorbed by the surrounding vege- 
tation. Archaeologists do not have the ability to detect vegetation changes with pure 
visual detection, as the chemicals cause the leaves to reflect differently in the electro- 
magnetic spectrum. This changed reflection might be detectable using classification 
(supervised and unsupervised), principal components analysis (PCA), and varying band 
combinations, which should first be tested on the known ancient sites in the region 
being explored. If the vegetation atop archaeological features is healthier or less healthy, 
the surrounding vegetation then NDVI will detect it. A focus should be made on the 
infrared (IR) part of the spectrum as vegetation will be the most likely indicator of 
ancient remains. These remains may include: Structures (Willey 1990), extensions of 
previously known sites, lakes, water reserves, and swampy areas associated with settle- 
ments. Broader issues of site abandonment can also be considered. In the Amazon Basin, 



Figure 5.2 Rainforest in Brazil (scale 1:11328 m), showing extensive illegal logging and defor- 
estation, which has destroyed many archaeological sites around the world, 2008 image 
courtesy of Google Earth™ Pro. 

Landsat imagery revealed an abandoned floodplain, now covered with forests and lakes. 
It would have been difficult to assess the overall landscape changes without the help 
of missionary writings. Combining this information with the satellite data helped the 
team understand past patterns of warfare (Parssinen et al. 1996). 

RADAR imagery, including SAR and SIR-C, may work to detect potential canals 
and other linear features. Archaeologists should exercise caution when examining this 
data, as linear patterns may be larger than actual features and the overall resolution of 
sensors may be too poor to detect anything. As with all archaeological site detection 
from space, features must be detected on the ground before any claims can be made 
(Pope et al. 1993). There can be significant noise from RADAR imagery, which can 
confuse even experienced remote sensing eyes. The L-band on SIR-C may aid in feature 
detection, but its low resolution may not detect smaller features. In rainforest areas, it 
may be of value for archaeologists to invest in high-resolution RADAR data, especially 
in areas where canals are well known, such as northern Belize and the Candelaria area in 
Campeche, Mexico (Pope and Dahlin 1989). LIDAR imagery will have much to offer the 
archaeology of rainforest regions, especially with its ability to see beneath dense canopy 
at high resolutions. LIDAR will probably contribute the most to the archaeology of 
rainforest regions over the next five years because it can detect archaeological features 
that would otherwise be unrecognizable due to vegetation cover. 



Figure 5.3 Colosseum, Rome, Italy (scale 1:82 m), 2008 image courtesy of Google Earth™ Pro. 

Urban (modern) 

Urban areas covering archaeological sites present a great challenge to archaeologists 
who wish to use satellite imagery. Human beings generally have great continuity of 
occupation, with many of the world's great cities (Rome, Athens, Cairo, etc.) covering 
layers of ancient debris (Figure 5.3). The ancient remains of a town may be deeply 
buried beneath modern buildings, with layers of concrete, piping, and wires obscuring 
site detection. Even on the ground, methods such as ground penetrating radar may not be 
able to detect the buried remains. Urban expansion is a global issue, which both helps 
and hinders the process of archaeological site discovery. Any time construction takes 
place in an old city, it is likely that ancient remains will appear. In the USA and parts 
of Europe, strict regulations govern the process of construction. Archaeological cultural 
resource management firms need to excavate the area in question to map and record any 
remains prior to construction. In other parts of the world, however, the same rules and 
regulations are not in force, and much has certainly been lost over the past 50 years. 

Diverse remote sensing strategies must be employed when attempting to detect 
archaeological sites from space in urban settings. If a city or large town is suspected of 
being atop an ancient site, Landsat, SPOT, and ASTER imagery can detect land cover land 
use changes, which can be used to determine general rates of urbanization and its effects 
on archaeological sites. Religious structures can often be centrally located in a town, 
and may be in the oldest part. That knowledge can help with knowing where to look for 
ancient sites. Classification techniques may aid in differentiating between an older part of 



town and a newer part, with the assumption that a newer part of town will have different 
building materials. From space, it is not possible to see beneath modern structures. One 
can assess the general topography and search for the parts of a town site with higher 
elevation by draping satellite imagery over SRTM data. Using high-resolution data in 
parks, exposed lots, or playgrounds may show crop-marks in the infrared range, which 
may be further revealed through frost or heavy rains. For example, on soccer fields in 
Cambridge, UK, the author noted that crop-marks of circular structures appeared more 
strongly following a period of rain. Edge detection on Quickbird imagery may also help. 
If a known archaeological site is partially covered by a settlement, then archaeologists 
can determine the spectral signature and use classification to discern other exposed parts 
of sites (Parcak 2007b). 

Rural or forested 

The term "rural" does not refer to a uniform landform type. In general, rural areas can 
be defined as areas that are: without dense occupation, forested areas, grasslands, or 
partial deserts. The latter two categories are discussed further in this chapter. Even more 
modern dwellings in rural regions will aid in archaeological site location, as they may be 
strategically placed near long-used resources. These may include water sources, forests, 
or other natural resources. Using ASTER, SPOT, and Landsat to examine land cover 
change through time will help see any general landscape trends. Like in rainforest areas, 
vegetation vigor may vary on top of dense ancient remains. Smaller ancient sites, such as 
shell middens, will not be detectable from space if obscured by dense vegetation. Rural 
occupations might be near specific geological formations, such as mountainous terrain, 
good for feeding sheep or goats. Human beings affect landscapes in so many subtle 
ways, with terracing, housing abandonment, and their associated waste products, that 
sometimes only LIDAR has the ability to see beneath the dense vegetation. Structures 
deteriorate and decay over time, while their appearance from above is stabilized by 
vegetation, and can be detected with relief variations. Filtering a digital terrain model 
created with LIDAR in a forested area can show solid ground versus vegetation cover, 
slopes, and other ancient remains not visible from aerial photographs alone. The key is 
slower moving platforms to allow more of the pulses to reach the ground (Doneus and 
Briesse 2006b). Quickbird and IKONOS data may aid the detection of cropmarks and 
other buried features once a team has chosen a specific area with known archaeological 
generally remains. Starting with the known and working towards the unknown in large 
rural areas is the best way to proceed. 

Mixed (urbanlrural) 

Transition zones may exist between urban and rural areas, as many cities can be a mix 
of both rural and urban zones. Using a combination of approaches is the best way to 
detect sites in mixed environments. When sites are located in urban areas, archaeologists 
should use urban detection techniques, and vise versa for rural areas. 


Deserts present a unique set of challenges to archaeologists. They are, by nature, arid 
places, yet many past groups lived in regions now classified as desert. Some of these 



5.4 Merv, Turkmenistan (scale 1:109 m), showing the great and small Kyz Kala, 2008 
image courtesy of Google Earth™ Pro. 

current deserts used to be wetter grassland areas, while others have been deserts for some 
time. People traveled extensively in desert areas following trading routes such as the Silk 
Road, or along paths in Egypt's Western Desert used as military access routes (Darnell 
2002). People could travel along desert roads as part of caravans, to access trading depots, 
military outposts, camps, or processional routes. Around the archaeological site of Merv 
in the Kara Kum Desert (Figure 5 .4) archaeologists used varying satellite images to map 
cities along the Silk Road (Sykes 1997). These past peoples would have settled along 
the Silk Road permanently, while other cultures had varied relationships with desert 
landscapes. For people like the ancient Egyptians, the desert was "deshret," or the red 
land, the opposite of the fertile Nile Valley, and a place to be feared and worshipped. 

In desert areas, people would have needed ready access to water and food, if the 
distances traveled made it difficult to transport these items themselves. Water could be 
found by digging deep wells, or by having access to oases, seasonal lakes, streams, or 
more semi-annual bodies of water that may have flowed more strongly in the winter 
and spring seasons. If past groups stayed longer term in a desert area, then they would 
have needed some type of permanent shelter. Trails or paths are additional features 
archaeologists can search for in desert areas. If groups permanently resided in a place 
removed from food sources, they may have had to support their own agriculture. SIR-C 
and SAR data have a history of being used to detect buried water sources, especially the 
so-called "radar rivers" in Egypt's Western Desert, which used to be savannah land in 
the late Pleistocene Period (ca. 130,000-10,000 B.C.) (McHugh et al. 1988). 



Overall landscape modeling should be done with SRTM data, as depressions can lead 
to the discovery of former water sources, including wells. Using NDVI on ASTER, 
Landsat, or SPOT data may help locate seasonal vegetation bursts (Karnieli et al. 
2002) or modern vegetation watered from old wells and water sources (Parker et al. 
2006). High-resolution imagery may even detect modern wells associated with now 
defunct riverbeds. In one study conducted to produce a record of Holocene surface 
water extension in a desert region of Mali, spectral data detected Holocene paleolakes 
fed by annual runoff between 7000—2000 bc (+/ — 500 years). Classification allowed 
a minimum area of the lake to be reconstructed (Lambin et al. 1995). Using Landsat, 
RADARS AT, and SIR-C imagery, a team of geologists mapped the Kharga depression in 
Egypt's Western Desert, showing that it was a main area for ground water accumulation 
(Robinson 2002). 

When searching over a broad area for past remains in a desert, a range of imagery 
will be needed. SRTM data can be draped over ASTER imagery for general landscape 
modeling to find the edges of past water sources (Bubenzer and Bolten 2003). Old traces 
of fields may appear in Corona imagery, or aerial photographs, or through edge detection 
with Quickbird imagery. This may be seasonal, with some vegetation growing atop 
field boundaries in winter months (Okin et al. 2001). Modern groups may be seasonal 
in terms of where they live and herd their animals, so studying these groups may give 
clues to past subsistence patterns and what might be able to be detected from space. Too 
much sand may be covering past remains, and the size of the past building will vary. 
Spectral signatures of known sites in the area being explored should be obtained for 
comparative purposes, whether the material used included stone, mudbrick, straw, or 
other materials. The size of the past structures and features will determine what satellites 
are most appropriate to use. 

Detecting past trails from space may be harder, especially as so many ancient trails 
are reused in modern contexts. Landsat, SPOT, and ASTER may reveal larger desert 
trails with classification, PCA, and band combinations. Edge detection with IKONOS 
and Quickbird may aid in picking out smaller trails. If ancient peoples lined their paths 
with stones or other material, they will have a distinct spectral signature, and if compact 
enough or filled with enough past debris, the roads may retain water for longer periods. 
This can be detected with NDVI during winter and spring months as vegetation may 
concentrate along these paths. For overall landscape visualization and recreating ancient 
routes, SRTM data can be used in a form of techno-ethnoarchaeology: Archaeologists 
can determine strategies past peoples devised to travel along desert paths, and recreate 
ancient routes based on any known, historic texts that may exist. In Egypt's Western 
Desert (Figure 5.5) one team studied routes between the oases using ASTER and Landsat 
data. These routes, important during the late Roman Period (ca. 400—800 ad), traversed 
landscapes where the precipitation was less than 5 mm per year with a few wells lining 
the caravan routes. The team recognized that additional routes had yet to be located, 
mapped, and surveyed (Eichhorn et al. 2005). 


Grassland regions may be the locations of former forests or other types of landscapes. 
Cultures living in grassland areas would have practiced either grazing or hunting animals 
such as buffalo. Past peoples in grasslands would have had seasonal campsites, but the 



Figure 5.5 Western Desert, Egypt Landsat (scale 1 :40000 m), note ancient desert paths are visible 
from space, image courtesy of NASA World Wind. 

relationship of these occupations to natural resources needs to be well understood when 
using satellite imagery. From space, archaeologists might find ancient migration routes 
of animals or cropmarks showing evidence of seasonal settlements. This would be difficult 
if the structures did not require any foundations or did not disturb the soil in any way. 
Historic-period houses may lend themselves more to this type of detection. It may be 
that past groups living in grassland areas did not alter the landscape enough for it to be 
detected from space thousands of years later (Roberts 1990). Certainly, ancient groups 
would have followed water resources, so crop marks or specific spectral signatures may 
reveal now-silted up river sources or other bodies of water. These may be seasonally 
detected after periods of heavy rain or in periods of heavy vegetation growth. Landsat 
and ASTER imagery can classify landscape types, as ancient remains might be detectable 
in specific classes of land. Old roads may be visible on Quickbird or IKONOS satellite 
imagery with edge detection, while ancient migration routes may be buried deeply 
beneath the soil. 

In general, archaeologists will likely be carrying out broad landscape analysis, so 
they will need large landscape coverage with coarser resolution satellite imagery. Large 
communities in countries such as Mongolia may have occupied specific areas for long 
periods of time, altering the soil enough to change its spectral signature, thus making 
it detectable with multispectral satellite imagery. In a more modern study in Mongolia, 
a team used Landsat imagery, NDVI variation, and DEMs to study spatial grazing 
patterns. The team found that pastoralists concentrated around permanent settlements. 
Political changes in Mongolia in the 1990s from socialism to capitalism caused a collapse 
in grazing pressure (Okayasu et al. 2007). This study shows the importance of how 
centrally located and peripheral groups might have functioned in the past. 



Mountainous I hilly 

Past peoples would have occupied hilly and mountainous regions for many reasons, 
including: defensive purposes, mining, general settlements, or settlements in association 
with religious structures or cults. Uneven terrain makes it difficult to search for remains, 
as they may be partially obscured by overhanging rocks. Obtaining SRTM data and 
draping Landsat imagery over it is the first step in analyzing archaeological material in 
elevated areas. The elevations of known archaeological sites and features can be compared 
to other suggestive signatures. Settlements and features may appear at approximately 
the same elevation, or there may be linear relationships between site locations. ASTER, 
SPOT, and Landsat imagery can classify mountainous landscapes to see if actual sites have 
definite spectral signatures, or if known sites appear connected with discreet geological 
formations. Paths to and from sites may be located with higher resolution imagery; 
everything depends on the purpose and function of the structures being examined. 
Ancient and modern mountain paths may be impossible to distinguish from one another 
(especially in association with mining regions still being used today), while reuse will 
only be determined by collecting material culture on the ground. 

Google Earth™ Pro may aid in viewing sites and features in 3D with high-resolution 
data (Figure 5.6). Hyperspectral data may be the most effective tool when working 
in a mining region, as features may be found in association with specific minerals. 
Overall landscape visualization in 3D will help archaeologists assess why cultures built 
in mountainous regions in the first place. Even creating maps of mountainous regions can 
aid teams in creating base maps for their topographic surveys (Romano and Schoenbruan 
1993). Religious structures in hilly areas will vary in purpose and function: Whether to 

Figure 5.6 Mt. Katahdin, Maine (scale 1:3000 m), 2008 image courtesy of Google Earth™ Pro. 



be seen, such as the Bamiyan Buddhas; or to remain hidden, such as cave dwellings for 
ascetics. "Hilly" includes wadi or valley regions, such as Petra, which are even harder to 
map, as satellite imagery can be taken at an angle, which may prevent archaeologists from 
seeing the base of wadis. Additional problems in detecting mountainous features include 
surface topography, erosion, faults, and sites covered over by avalanches, mudslides, or 
earthquakes (Toprak 2002). 

Archaeologists have noted that working in mountainous terrain can be problematic, 
which is why a range of image types should be employed. Vegetation can mask soil 
types covering ancient remains, again making the case for hyperspectral data. In the 
Itanos area of Eastern Crete, a team used Airborne Thematic Mapper (ATM), Compact 
Airborne Spectrographic Images (CASI), and LIDAR data to map the harbor region. 
This area was important for trade between Crete and the Eastern Mediterranean dur- 
ing the Greek through Byzantine Periods. Rains were found to be at higher levels 
between one to three meters below the surface. Stones used for the ancient buildings 
were noted to be the same as the stones lying on the ground surface. Early morning 
was the time the archaeologists took the imagery, when heat differences with buried 
features and the ground were the greatest. After correcting the data, the team used 
NDVI and classification. The CATA and ATM data showed some anomalies, and the 
LIDAR data revealed one wall. The remote sensing showed abandoned terraces as well 
as a threshing floor. The authors commented on the problems of using remote sensing 
to detect subsurface remains with many mixed pixels. One wonders why they did not 
use Quickbird imagery. In the areas the team was investigating, it would not have been 
too costly and would have provided them with high resolution for subsurface feature 
detection, and could have been combined with the LIDAR data. (Rowlands and Sarris 

Mixed landscapes 

Most remote sensing will include mixed landscapes, which will require archaeologists to 
have differentiated approaches (Urwin and Ireland 1992). This is why broad multispec- 
tral datasets (such as ASTER, SPOT, Landsat, and Corona) are needed. Archaeological 
sites will appear differently in each landscape site, so approaches need to be adapted 
accordingly. Similar features in different landscape types may have identical spectral 
signatures. Major landscape projects surveying across different landscape types will use 
satellite imagery types they determine to be most appropriate (Palmer 2002a; Campana 
etal. 2006;Powleslande/*/. 2006). 


Coastal settlements may be either permanent or seasonal, but their overall purpose and 
function will vary (Franco 1996). Such settlements may be trading towns, simple coastal 
fishing villages, or located within fortresses, which may have some type of docking 
system, harbor, or shipbuilding areas. Structures will also be built of varied materials. 
How these can be detected from space depend on this material, as well as the waste 
products left behind by past groups. These would most likely include fish bones and 
shell middens. Actual places of settlement may not be visible from space if they are 
obscured by dense coastal vegetation. Many coastal towns have remained important 



Figure 5. 7 Vanua Levu, Fiji: (a) 3-2-1 Landsat image showing region with dense cropmarks one of 
which can be seen in the circle; (b) Quickbird satellite image showing fort structures, 
images courtesy of NASA and Google Earth™ Pro. 

through time, so ancient parts of towns may be covered over partially or entirely by 
modern structures. 

Multispectral or hyperspectral imagery will work best in detecting the spectral sig- 
natures of past settlements or, through NDVI, PC A, and classifications, will detect 
vegetation atop ancient remains. In Fiji, on the island on Vanua Levu (Figures 5.7a 
and 5.7b) Landsat ETM+ data revealed the spectral signatures of fortress-like structures 
that appeared more clearly on Quickbird data (as observed by the author). These inland 
sites were likely built with an increased need for more defensive positions. Clusters of 
over 100 structures were seen. This was unusual on the Landsat data, as the walls of 
the structure were not more than a few meters thick, showing how smaller features can 
appear on coarser data if the spectral signature is distinct enough from the surrounding 

Ocean and sea edges change constantly, with many ancient sites being partially or 
entirely washed into the sea. This can clearly be seen along the coast in Ireland, or at the 
edges of sites in the Mediterranean, where many earthquakes have sunk cities such as 
Alexandria. Low tides may reveal partially washed away sites, which makes knowing the 
timing of tides important when ordering high resolution imagery: One would wish to 
place an order for imagery when the tide is low in the morning. Can waterlogged features 
be seen from space? The author observed remnants of what appears to be old docking 
systems in Brittany, France, using World Wind. Satellites cannot see underwater due 
to the reflective properties of water. Settlements along the coast may have their own 
spectral signatures, which may or may not be affected by tides. SRTM data can model 
coastal sites, and predict the elevations where similar sites may appear. 

Waterlogged! wetland (including swamps) 

This is an area that has received virtually no attention from archaeologists using satellite 
imagery. Waterlogged ancient sites may or may not be detectable from space, depending 
on how much of the ancient site remains exposed today. Features could be too deeply 



buried beneath debris to be located. If people lived in waterlogged areas, then they 
would have needed to live on elevated areas in structures built above watercourses. High 
humidity may cause such elevated platforms to disintegrate over time. Broad landscape 
classification with SPOT, Landsat, and ASTER will help define wetland or swampy areas 
in broader landscapes, where archaeologists will search for features. For example, in past 
swampy areas in England, ancient groups threw in what were likely sacrificial victims, 
known today as "bog bodies." An archaeologist identified peat deposit Landsat and 
classification data, which represent past swampy regions where additional bog bodies 
might be located (Cox 1992). 

Former swampy or waterlogged regions may now be filled in with earth, and therefore 
detectable. If not, there may be some elevated areas where ancient remains might be 
found. SRTM data will detect general landscape topography, and may help archaeologists 
zoom in on potential areas to examine. In swampy regions, issues of access might be 
a problem, for how might one search for sites in an almost entirely waterlogged area? 
Vegetation remains may appear with different colors when examined with multispectral 
imagery. Even if exact coordinates are known for potential sites, an archaeologist can 
canoe or kayak to reach the sites, the GPS units may malfunction with dense foliage as 
described above. PCA, classification, and hyperspectral data combinations may detect 
vegetation differences above ancient sites, but if an area is still too waterlogged, those 
remains will probably not appear from space. 

Alluvial plain 

Alluvial plains differ in size and shape across the globe. How much the rivers in the 
plains would have flooded in the past, or how much the rivers continue to flood in the 
present, depended on (or depends on) climactic conditions and annual rainfall patterns. 
Some alluvial plains have many ancient remains visible today, while others have partially 
silted over sites, totally silted over sites, or a mix of all three. Alluvial plains were ideal 
areas of settlement for past peoples, given the large amounts of nutrients in the soils 
for agricultural purposes. Alluvial regions were also ideal for transportation, livestock 
feeding, and trading, as past settlements would have concentrated along rivers in alluvial 
plains that connected much larger bodies of water. These settlements would have varied 
in size, with a central area of settlement (either a regional or local capital) surrounded by 
a hinterland of smaller sites. These sites may follow canals or rivers long since silted over, 
so identifying past water sources is the central goal when working in alluvial regions. 

Alluvial plans can first be mapped with Landsat imagery draped over SRTM data 
to gain a general sense of topography. Slightly higher areas of elevation may indicate 
former places of settlement. Landsat, SPOT, ASTER, and other hyperspectral data may 
reveal spectral signatures of ancient sites, which is why it helps to have known sites 
in the region for testing techniques such as classification, PCA, band combinations, 
and others. Corona imagery is important to use for landscapes now largely altered by 
modernization. Many landscape changes have occurred in the past 30 years, so Corona 
imagery or aerial photography, dating prior to 1 97 5 , may show features now destroyed by 
increasing agricultural practices in alluvial regions. It may be difficult to differentiate 
archaeological site material from the silt surrounding it, but extensive past human 
occupation debris should alter a site's spectral signature sufficiently for it to be detected 
from space. 



Figure 5.8 Tell Sariri, Delta (scale 1:86 m): (a) showing cropmarks; and (b) cultic structure, 2008 
image courtesy of Google Earth™ Pro. 

These sites will range from tiny settlements to massive cities, or may appear to be a 
small city based on surface remains, yet magnetometer survey will reveal a major site 
long since silted over. This is where crop marks may aid in determining a site's former 

Once archaeologists have identified potential sites, they can purchase higher resolution 
imagery to view crop marks, which may be viewable on Google Earth™ or World Wind. 
For example, an archaeological site declared as "destroyed" in Egypt's East Delta, Tell 
Sariri, had clear crop marks that showed the potential foundations of a cultic structure 
(Parcak 2009) (Figures 5.8a and 5.8b). This was a site archaeologists said was destroyed 
over 100 years ago, and shows the power satellite imagery can have in reconstructing 
supposedly "lost" landscapes. 

Assessing changes in alluvial plains is made difficult by exponentially increasing 
changes (Brivio et al. 2000), which is why land use land cover change analyses are so 
important in alluvial areas. SIR-C and RADAR imagery may aid in detecting past allu- 
vial courses, and will help archaeologists reconstruct rates of change. Dams will affect silt 
discharge, which, in turn, will affect how much river courses shift. If rivers rise because 
of dams, then large alluvial areas may disappear, and satellite imagery may be the only 
record of areas under water. It will not be possible to ground truth those sites, but at least 
identifying features will contribute to a better archaeological understanding of a region. 

Rivers would have had specific periods of maximum discharge, yet the overall amount 
of discharge would have varied through time. This discharge, through time, would 
have affected ancient site placement, for if a river started to cut into a settlement, it 
would have moved partially or entirely. Modern floodplains have levees, active channels, 
crevasse splays, and abandoned channels, and some of these factors, such as levees, may 
be mistaken from space as being ancient occupation areas. Levee height will vary in 
each floodplain, and may represent ancient levees worn away through time. In Lower 
Mesopotamia, which was irrigated mainly by the Euphrates (the Tigris was more violent 
and less predictable), SPOT imagery and aerial photographs identified ancient river 
courses, abandoned channels, and crevasse splays associated with early agriculture. Early 



groups of people could change channels to support irrigation, thus aiding the growth of 
early city states. These splays show a sedimentation rate of 1—1.8 mm per year between 
6400-1000 bc (Morozova 2005), similar rates to the Nile Delta (Butzer 1976) but lower 
than Middle Egypt, which seemed to have rates of 5 mm per year (Parcak 2007a). 

With Landsat, SPOT, ASTER, RADARSAT, and Corona imagery, a team of archae- 
ologists studied landscape change in the region surrounding the region of Nippur and 
Lake Dalmaj in Iraq (Southern Mesopotamia). This region consists of floodplains, aban- 
doned floodplains, and some dune areas, with no survey done since the 1990s. Artificial 
coloring of grayscale Corona imagery aided the team in landscape assessment. With 
significant modern land use changes, the team used a number of remote sensing tech- 
niques to make past sites appear. Histogram equalization and linear stretching revealed 
additional canals, while the RADAR imagery revealed canals, dunes, levees, and agri- 
cultural systems. Even with speckle reduction, the RADAR imagery needed to be used 
with the SPOT and Landsat imagery. These images were classified into 25 categories 
to examine past versus present drainage patterns, then integrated into a GIS. Corona 
imagery showed that land changes through time revealed more levees as dunes shifted. 
33 previously unknown features that seemed likely to be ancient appeared, but ground 
truthing was impossible with the ongoing Iraq conflict (Richardson and Hritz 2007). 


Tundra may be challenging for remote sensing work, depending on how much of the year 
it remains frozen. As there are short growing seasons, archaeologists can take advantage 
of the window when vegetation is starting to emerge (Figure 5 .9) or when frost is starting 
to appear. Using high-resolution imagery, either crop marks or frostmarks may appear. 
Densely settled seasonal encampments may have their own spectral signatures, detectable 
during warmer months. It is worth classifying Landsat, ASTER, or SPOT imagery prior 
to purchasing higher-resolution imagery to see if any specific landscape class types 
show potential archaeological remains. This depends on the overall survey size. It may 
help comparing the spectral signatures of modern groups with potential areas for past 
remains. When examining areas with indigenous groups, the remains these groups may 
leave behind before shifting camps may show how past peoples affected the landscapes. 
Also, archaeologists can examine where these groups live in relationship to food, water, 
and transportation routes. If teams know that settlements have not altered greatly in 
hundreds of years, it might help them think about groups of people even further back 
in time. Global warming is also affecting settlements in tundra regions. With warming 
temperatures, frostmarks may not be around for as long, and remains preserved by the 
frost may have deteriorated. ASTER imagery, with three thermal bands, can determine 
how much warming may have affected past remains. Hyperspectral imagery can also 
assess known remains in the thermal band, and see how warmer temperatures may have 
affected potentially "new" sites as well. 

Frozenl glaciated areas 

Frozen areas will likely completely obscure past remains, especially areas covered with 
glaciers that would have covered over traces of human remains from the last interglacial 
period. There may be evidence of seasonal encampments, but if covered over by ice, it 



Figure 5.9 Siberia (scale 1:4688 m), 2008 image courtesy of Google Earth™ Pro. 

will not be possible to see them from space at present. Archaeological remains from 
frozen areas, like that of Utzi, or "The Iceman", can be discovered randomly. There 
may be many other frozen people or past remains in glaciers, but the chances of high- 
resolution imagery detecting them are small (Utzi would have been measured by one 
by two Quickbird pixels). Certainly, archaeologists can scan frozen areas to check for 
anomalies, and with global warming, larger areas hidden by glaciers appear each year. 
These areas can be detected through land use land cover change on coarser resolution 
imagery, and then searched more carefully with high-resolution imagery, but only if one 
has a good idea of what features might appear. These will vary depending on what part 
of the world the work is taking place. 

Feature types 


Cities, villages, towns, seasonal campsites (varying landscape types), 
walls/architecture, industrial installations (inside and outside of settlements, 
crossing zones) 

All villages, towns, and cities will vary in size. What may be considered a major city in 
one archaeological region may be a village in another. Scale of construction will depend on 
local, regional, and national factors, while landscapes will continue to play a considerable 



role. Resources of cultures will vary through time, and growth or collapse of civilizations 
or empires will present challenges for finding archaeological sites with satellite imagery 
as site placement changes. Through locating important natural resources, satellites will 
aid archaeologists in determining how and why settlements and features have changed 
location through time. 

No matter the size, all occupied areas will contain some form of architecture, though 
it will vary in size, shape, and material. Identifying what material past peoples used 
for their occupations will aid in determining what remote sensing methods will work 
for feature detection. Each material may have its own individual spectral signature, 
or will alter how vegetation and surrounding soils reflect light. Most occupation sites 
have mixed architecture, depending on the function of the structures. A temple will 
likely be built out of better (and longer-lasting) material than a poor family home. 
Material durability also needs to be considered in comparison to the overall surrounding 
environment. A mudbrick structure preserved in an ancient layered settlement in Egypt 
above the floodplain will be easier to detect from space than the same type of structure 
in a still-active, low-lying floodplain area. Having extensive knowledge of known sites 
and architecture types will determine what similar features might appear at other sites, 
and what their spectral signatures might be. 

Architecture of varying size has appeared on many types of satellite imagery. 
If walls are sufficiently thick, or are easily discernable from the surrounding soils 
with a distinct spectral signature (Ben-Dor 1999), then coarser resolution Landsat 
or SPOT imagery will be able to detect them. Thick features covered by agriculture 
will be viewable on Google Earth™ or World Wind with crop marks. If imagery 
is taken at the right time of year, then a number of subsurface features will appear. 
Knowing the correct time of year to purchase imagery will help to map subsurface 
features with Quickbird imagery. At Tell el-Amarna, Egypt's most well-preserved 
ancient city, Quickbird satellite imagery detected a number of subsurface architec- 
tural remains. Most of the mudbrick walls for houses at Amarna in the central city 
are less than 1 m in thickness, with a large part of the size covered in debris from 
past excavations. Using edge detection revealed several unexcavated structures (Parcak 

Town layout may be a factor in satellite mapping. Many urban centers, especially 
if connected to state governments, were planned, or could be laid out according to 
cosmic patterns or diagrams. Some streets may be straight (Thakker 2002) (Figure 5.10), 
while others may meander in a seemingly random pattern. Street directions may vary 
depending on where in the town they are located. A number of ancient cities would 
have had central main streets or an axis. These features, even if filled in today, would 
likely be large enough to be detected from space on several satellite image types (if not 
already mapped). If archaeologists have planned well-known archaeological sites, the 
remote sensing may reveal a small to major part of the site covered by a modern town 
or agricultural fields. Knowing the orientation of structures and building types will aid 
in mapping the newly discovered features. 

Detecting "cities" from space should not be an outward goal of archaeological remote 
sensing projects. First, one can never know what one has found until ground- truthing 
and excavation are performed, unless a great deal is already known about archaeological 
sites in the region. Settlements can be obscured by shifting sands, rainforest growth, 



Figure 5.10 Mohenjodaro, Pakistan (scale 1:250 m), note detailed street plans, 2008 image 
courtesy of Google Earth™ Pro. 

oceans, forests, and deposits from rivers. How archaeologists decide to detect the 
occupation areas is more a function of landscape type than settlement type. Coarser 
resolution imagery is best if one is working in a broad area, while Corona imagery 
and aerial photographs will give a good sense of what the landscape was like prior 
to major urbanization (if it occurred post- 1970s). Higher-resolution data should be 
purchased once a team has decided to focus on a specific site or sites detected 
from coarser data, or archaeologists want to attempt subsurface feature detection on 
known sites. 

Ancient settlements were affected by landscape processes occurring at different rates 
through time (Kamei and Nakagoshi 2002), but would have been most affected by 
changes to water and food sources. In Mumbai and Calcutta, maps at varying scales 
in addition to SPOT and IKONOS imagery detected water catchment areas attractive 
for settlement (Som 2002). Water sources may have dried up quickly or over hundreds 
of years, changing settlements from permanent to seasonal to nonexistent. By map- 
ping areas where water once flowed, archaeologists can follow the paths of these water 
sources, or perhaps locate vegetation signatures that mark past water sources still used 
today (Mumford and Parcak 2002; Parcak 2004a). In China, RADARSAT and Landsat 
4-3-2 imagery detected a canal silted over in Jin Dynasty (ca. AD 1115—1234), along 
which major cities prospered during the Song Dynasties (ca. ad 605—1279) due to trade 
(Xin-Yuan et al 2004). 



Seasonal campsites present more of a challenge for satellite archaeology. Many seasonal 
encampments would have had temporary buildings (i.e. tents or yurts), yet if an area 
were occupied over enough seasons, enough waste debris would accumulate to alter 
the landscape enough to be detected from space. A number of permanent yet seasonally 
occupied structures exist at archaeological sites around the world. For example, a military 
garrison may only need to occupy a stone-built fortress during a certain time of year. 
These structures would be much easier to locate, especially if located in more rural or 
removed areas such as deserts where their spectral signatures would stand out. 

Archaeologists need to focus on past and present factors affecting ancient settlements. 
Using a combination of IRS, SPOT, and Landsat imagery, archaeologists mapped two 
fort sites in Hyderabad (Golconda Fort and Purana Qila). Satellites aided the team in 
specific site location, spatial distribution of archaeological sites, and the general extent 
of the monuments. Golconda Fort covers an area of 317 hectares, and was important as 
a medieval fort as well as laying the foundations for Hyderabad city. Purana Qila was 
also a medieval city. Archaeologists had serious concerns regarding urbanization in both 
sites, with urbanization increasing 82 percent in 15 years at Golconda Fort and 667 
percent in 15 years at Purana Qila (Balaji et al. 1996). 

Industrial areas within settlements or cities will have distinct spectral signatures if 
they are large enough and their waste products occur in large quantities. How these 
sites may be detected from space depends on the type of fuel past peoples used and 
what byproducts might have been produced. Slag may litter the surface of an archae- 
ological site enough to be detected through space, either through a distinct spectral 
signature, or through color differences in visual imagery. In southeast Norway, archaeol- 
ogists used LIDAR to detect forested sites dating to AD 650-1350 (late Viking-Middle 
Ages). The team created a digital terrain model to detect concave and convex anoma- 
lies, which included 17 possible iron extraction heaps. Fieldwork showed that five of 
the 17 sites were indeed iron extraction heaps, with the others being natural structures 
or errors. The ability of the imagery to detect iron sites depended on the overall size 
of the remains, the density of the ground iron, and the time of the survey season (in 
either autumn or spring, when no leaf ground cover obscures features) (Risbol et al. 


Footpaths, roads, trails, former river courses (as a linking entity), canals 

Paths, roads, and trails all differ in meaning and function. Some exist as a main trans- 
portation route between major settlements, while others may be a processional route or a 
way for past peoples to access types of natural resources. Most past linking routes would 
have had multiple layers of meaning, many of which may no longer be understood by 
archaeologists. Differentiating between the sacred and the profane on roads or trails is 
not an easy exercise when only a small part of the paths survive today, and archaeolog- 
ical teams may not understand a basic "to" or "from." Some past peoples lined their 
roads with bricks, stone or wood, working to keep them clear of debris, while others 
allowed human or animal traffic to trample paths flat. Trails, paths, and roads also vary 
in width and length (Argotte-Espino and Chavez 2005), and modern reuse may present 
the greatest challenge for remote sensers to locate "ancient" paths from space. Meaning 



in landscape is closely connected to the meaning of past paths and trails, as they would 
likely have been closely connected to major landscape features. 

In southeast Utah, an archaeological study using IKONOS and Earlybird (another 
high resolution satellite image), detected a series of 10 m wide Pueblo trails in a network 
extending for 100 km. On the ground, the paths could only be visible to the well- 
informed eye. The trails connected many ancestral pueblo settlements, but could have 
predated the houses. They consisted of straight roads that did not pass around canyons or 
hills, but instead, traveled through them. Use of the IR band helped to identify the trails 
the best. The team could not connect any one meaning to the trails, as they were likely 
associated with trade, social interactions, politics, and general communication. Whether 
the trails had a religious purpose could not be determined. Locating archaeological 
features along the trails proved difficult because of issues with general destruction, 
animal grazing, erosion, bikes, and off-roading (Williamson et al. 2002). 

Detecting trails or paths in densely vegetated areas with satellite imagery is another 
archaeological challenge, especially in rainforest areas. In the Arena region of Costa 
Rica, Mayan occupation in areas further north was not dense. This affected human 
impact on the landscape. With low human occupation density, there was more of 
a reliance on community feasting and wild food gathering. In the Arenal region, 
a separation existed between cemeteries and villages. Understanding how the peo- 
ple of this area ascribed meaning along routes between the cemeteries and villages 
became one of the central foci of a satellite archaeology study. The study used color 
infrared imagery, IKONOS, RADAR, Landsat, SRTM, and TIMS imagery with range 
between 8.2 and 12.2 /xm, which detected temperature differences on the ground. 
Black and white aerial photography detected some paths prior to a 1973 mudslide. 
RADAR imagery detected many anomalies but had too much noise. Landsat imagery 
helped to pick out moisture differences, while pan sharpening the IKONOS imagery 
and using PCA, band combinations, filtering, and contrast enhancements helped to 
visualize the paths. Draping the imagery over SRTM data allowed 3D visualization 
(Figure 5.11). 

Starting in ca. 500 bc, the people of the Arenal region altered the landscape with 
the construction of paths, found to be 2 m or more in-depth, and constructed of 
earth and stone. These linear footpaths allowed the perpetuation of social memory, 
yet there were problems differentiating between modern paths and ancient ones. The 
"proper" way to reach a cemetery was along an entrenched straight path, thus creating 
a form of social memory connected to a sense of monumentality. Radiocarbon dating 
occurred with samples of tephra from the paths, as the Arena volcano has erupted 
10 times over the past 4000 years (Lambert 1997; Sheets 2004; Sheets and Sever 
2006, 2007). 

Locating canals and other linking bodies of water depends on what the bodies of water 
linked. Some may have been natural transportation or trading routes (thus providing the 
impetus for constructing the canal in the first place), while others may have facilitated 
more political contacts. The purpose and function of canals would have changed through 
time. Canals that lay unused in one period may be reused by another ruler, and thus 
dug out again. Dams and reservoirs would have altered these linking routes. Detecting 
past bodies of water from space requires virtually identical techniques as the alluvial 
chapter section, although higher resolution imagery may need to be obtained if the 
canals were not wide. Past land use practices would have affected usage of linking 



Figure 5.11 Arenal region of Costa Rica (scale 1 :2 196 m) note extensive cloud cover in imagery, 
arrow points to Arenal Volcano, 2008 image courtesy of Google Earth™ Pro. 

bodies of water, which is why a broad remote sensing and GIS approach is required 
(Niknami 2004). 

Temporary I border 

Way stations, caravanserai, frontier posts and border forts, quarries and 
trading centers, inscriptions (rock types) 


What constitutes the "fringe" of any culture will change through time as borders shift 
according to changing political situations. Associated border architecture will follow 
these shifts. How and why borders expand and contract through time is not necessarily 
something that remote sensing will be able to detect, as it is more closely connected 
to political or power changes. Environmentally connected border changes (i.e. fortress 
abandonment due to drought or lack of natural resources) may be detectable from space, 
especially if a major water source has disappeared. Trading routes will open up as cultures 
expand and as resources are secured. What constitutes a border area is not limited to land- 
locked sites: Coastal settlements are, by nature, along the frontier of any ancient culture. 
One question not examined yet with satellite archaeology is how remotely sensed 
imagery might be able to detect areas with rock inscriptions or carvings. Inscriptions 
could be left for religious purposes, as warnings (which would be located in more visible 
areas), or as a way to share information about an expedition (military or to obtain natural 
resources). Carved statues in rocky outcroppings could have religious meaning or be some 



Figure 5.12 Bamiyan Buddhas, Afghanistan (scale 1:65 m), from space one can view the 
destruction of the Buddhas caused by the Taliban, image courtesy of Google 
Earth™ Pro. 

other type of cultural signifier (Figure 5.12). Using hyperspectral satellite imagery such 
as ASTER, archaeologists could classify landscapes with known inscription information 
or associated carvings. These may appear at a certain elevation or in a specific geological 
formation, both of which can be detected from space. Modeling landscapes with SRTM 
data in conjunction with the ASTER analysis can give clues about potential paths past 
peoples might have taken to and from marginal regions. These paths may be formed by 
"connecting the dots" of known and newly discovered inscription data. 

Studying ancient quarries reused in modern times can be problematic, but associ- 
ated material culture found during ground-truthing will help define a modern mine as 
"ancient." Vegetation can be studied as a possible aquifer indication, and flow models 
can be created in a GIS. Old paths to and from mines can be created through digital 
elevation models, so archaeologists can gain a bird's eye view of how peoples may have 
planned their expeditions (Vermuelen 1998). Satellite imagery can aid in reconstructing 
past mining landscapes under threat of development. Egypt's ancient quarries are being 
mapped through the Quarry scapes project, which is using Landsat imagery and Google 
Earth™ to show ancient mines either being reused or under threat of reuse (Bloxam and 
Storemyr 2002). In Yemen, 70 km to the northeast of Sana'a, a detailed geological study 
of the region took place using multiple Landsat images, SPOT, and Quickbird imagery 
draped over digital elevation model data. The project took place in Jalabi, which was 
known for silver mining in the 9th century ad. This study located mines in specific 



geological types and at specific heights. The Quickbird data revealed ancient terraces 
(with palm trees and water sources) described by Al-Hamdani in ca. AD 1000. When 
in the field, the team studied archaeometallurgy, and analyzed the abandonment of the 
mines (Deroin etal. 2006). Ancient resource areas can be identified from space using their 
spectral signatures, which may aid locating the sites during survey (Martinez-Navarrete 
etal. 2005). 

One example of a culture's border area, constituted by multiple landscape types, 
includes coastal port areas. These would have been major centers for trade, travel, and 
all forms of political and economic exchange. Multiple satellite images would be required 
to analyze coastal landscapes as they represent a confluence of landscape types. Rivers 
flow into larger bodies of water; port sites may be on the edge of rockier terrain, and may 
have moved over time through sedimentation of river mouths. Along the Ambracian 
Gulf in Epirus, Greece, Landsat SPOT, RADARS AT, and ASTER imagery, combined 
with coring and sedimentological data, detected river mouth changes through time as 
part of the Nikopolis project (Stein and Cullen 1994; Sarris et al. 1996). Marine cycles 
would have exposed the submerged surface, which the archaeological team analyzed 
to detect buried areas and change processes. Archaeological sites could be buried, land- 
locked, or destroyed, and this project was able to time past changes and their extent. The 
project allowed for variable rates of change. PCA and NDVI allowed the archaeologists 
to detect physical characteristics in the landscape, as well as overall landscape bright- 
ness, greenness, and wetness. PCA found more humid sediments, coastlines, lagoons, 
paleochannels, and swamp deposits, while the SAR data found likely meander scars. 
This allowed past landscape reconstruction in conjunction with the site coring (Vining 
and Wiseman 2006). 

Resource management systems 

Dams and reservoirs, resource areas (forests, fishing grounds, salt pans) 

These features are generally found in association with settlements, with resource areas 
being difficult to detect using remote sensing. Fishing and hunting areas are not easy to 
define, and would have taken place near most water sources or rural areas with animals. 
Reservoirs could have dried up over time, and would be detectable using the same remote 
sensing techniques to find rivers (Stafford et al. 1992; Bhattacharya 2004). 


Hunting or game preserves, zoological parks, botanical gardens 

Studying ancient installations from space is not something that has, to date, received 
any attention from archaeologists. Detecting game or hunting preserves, if located 
close to settlements, will probably not be possible, even with high-resolution satel- 
lite imagery. How past people may have defined their game preserves is not an easy 
question to answer even with detailed archaeological or historical data from landscape 
changes. Botanical gardens would have been supported by the elite and may be located 
in association with elite installations. This would be difficult to differentiate from an 
agricultural plot. 



Fields and plow marks 

Detecting past agricultural practices from space depends entirely on how much modern 
practices have affected landscape evolution. Thousands of years of continuous and inten- 
sive cultivation may erase traces of past agriculture. Deposits from rivers and weathering 
may also work to obliterate former traces of fields. Urbanization or changing landscape 
practices over the past 30 years make using past satellite imagery or aerial photographs 
essential for landscape reconstruction (Supajanya 1989; Harrower 2002). Some places, 
such as the UK or mainland Europe, are known for having better preserved field systems. 
Other places where one might expect such preserved systems to exist, such as Egypt, 
have virtually no traces of previous agricultural zones. 

Every region in the world will require different approaches to the detection of past 
agricultural areas using remote sensing. In the southeast Crimea of Ukraine, an area on 
the World Monument Fund's list of 100 most endangered monuments, the Chora of 
Chersonesos, was studied with Corona and Landsat imagery through a GIS. The team 
used stereo pairs of Corona imagery to create digital elevation models. Comparing the 
Corona imagery with classified Landsat imagery from 1988 and 1992 showed an increase 
in urbanization of 7 percent (Trelogan et al. 1999). Levelled landscapes can also prove 
difficult to assess. Although an archaeological team may detect hidden ridges and furrows 
with remote sensing, and medieval landscapes using digital terrain models, others could 
not be detected due to leveling of the region by farmers (Sittler and Schellberg 2006). 

Landscape palimpsests present additional problems (Beck et al. 2002). Just because a 
landscape was farmed in the past does not mean it was farmed throughout history. Areas 
can lie fallow for generations before being farmed again. Perhaps fields in a floodplain 
area were salted by Romans following an attack, and not used for 50 years. Instead, 
past people may have used the area to build homes, or animal pens. As the salts were 
washed out by inundations or rainfall and the nutrients were replaced, farming may have 
returned to that area. Archaeologists must think of landscapes in individual periods 
of time (Wilkinson et al. 2005) and as affected by ongoing political, economic, and 
environmental factors. Local field systems would have been intimately connected to local 
population sizes, which were, in turn, affected by economic and ecological constraints 
(Wilkinson et al. 199 A). This necessitates a holistic approach to landscapes. 

Corona imagery is the best imagery available to detect past changes in the Middle 
East, due to its high resolution and low cost. It can be compared with more modern 
imagery to assess overall landscape changes over the past 40 years. Archaeologists need 
to think about other ways of detecting past field systems, whether through testing for 
manure or additional inorganic remains that may be remains of fertilizing (Wilkinson 
1989). Using SRTM data to detect areas of settlement in floodplain regions may be 
difficult, as modern towns can cover past ancient sites (Sheratt 2004; Menze et al. 2006) 
or even field systems. 

Modern agriculture in inundated areas still allows the detection of large mounds, 
but the total area of past agricultural practices will not be possible to see with satellite 
images. Water sources connected to past fields are another story. Signatures of earthworks 
and canals may aid in reconstructing past agricultural patterns in areas with significant 
water resource management (Parry 1992). Even in areas where intensive agriculture has 



Figure 5.13 West Delta, Delta, Egypt (scale 1:36000), Alexandria is the large city along the 
western coast, 2008 image courtesy of Google Earth™ Pro. 

drastically altered past landscapes, satellite remote sensing can detect past water sources 
for settlements and agriculture. These studies can also help anthropologists understand 
present land use practices (Badea et al. 2002). Using Landsat imagery, SRTM data, 
and aerial photographs, a team of scientists detected parts of the Canopic channel in 
the northwest Delta of Egypt. The 36 km trace and the 20 km trace were part of the 
larger channel that provided fresh water to Alexandria and other ancient settlements 
(Figure 5.13). The most recent channeled segment of the Canopic flowed north to the 
archaeological site of Herakleion in the 1st century AD. With the lowering of the western 
Delta and accumulation of sediments, a new course of the Canopic channel developed. 
A total of six bends could be seen along the traces of the ancient channel. The ancient 
coastline shifted south at a rate of 22.5 km a year, with the channels extending 6 km 
north of the current shoreline, due to sea levels rising and sedimentation. The study 
also examined the Idelu Lagoon, now converted to agricultural land to support part of 
Egypt's rapidly expanding population (Stanley and Jorstad 2006). 

Past fields or field systems can be detected in similar ways to past settlements, through 
either cropmarks or frostmarks. Corona, Quickbird, or IKONOS imagery may all reveal 
past field systems with visual detection (in the case of Corona), or through the use of the 
IR band with filtering. LIDAR imagery also works well in detecting past field systems, 
in addition to settlements. A good understanding of the chemical properties of soil, 
weather patterns, and, most importantly, planting seasons, will aid in the detection of 
past fields, which may appear better in areas with crops than bare fields (Shell 2002). 




Graves and tombs, general mortuary architecture, shrines 

Detecting mortuary features is not an area that has, to date, received as much attention 
from satellite archaeology if there is not associated monumental architecture. Certainly, 
mortuary areas form an integral part of most settlement areas. If they are not a part 
of those settlement areas, then they will probably be located nearby. Some mortuary 
features will be easily detectable from space, while others will not. For example, bodies 
deeply buried in a stone shaft tomb might be able to be detected from space if there is 
a stone superstructure or other related architecture. Bodies buried beneath a home will 
not be able to be detected, for obvious reasons. Graves will be different shapes and sizes 
in each archaeological region, and will vary depending on the status of the individual 
(Figure 5.14). Mortuary superstructures will also vary. 

How archaeologists detect mortuary features from space depends on what they hope to 
find. Do ancient cemeteries have their own distinct spectral signatures? This is something 
to be determined by remote sensing analysis. If the tombs are built from the same material 
as associated houses, and the cemetery has a similar stratigraphy to the settlement area, 
then the same techniques one would use to find ancient settlements would apply. This 
includes running classifications, PCA, and filtering on Landsat, SPOT, ASTER, and 
Quickbird imagery. It remains to be seen if dozens or hundreds of bodies in the same grave 
would affect the surrounding soil's spectral signature. What about exposed areas of bones 
atop archaeological sites? As observed by the author on the ground, the "hill of bones" 


ft .* iM 


Figure 5.14 Arlington National Cemetery (scale 1:108 m), the small white dots are gravestones, 
2008 image courtesy of Google Earth™ Pro. 



at the site of Mendes in the northeast Delta would be large enough to be detected by 
high-resolution data. How the bone-covered small mound would be detected would, as 
in all remote sensing, be affected by moisture conditions. 

Smaller mortuary remains, including tombs or shrines, could be detected, but this 
would depend on their size. The naos at Mendes is easy to see on Quickbird imagery, 
while smaller shrines or stelae may not be visible. What about tombs such as in the Valley 
of the Kings? Entrances have been enlarged for tourism, and protective walls around the 
tomb entrances prevent floodwaters from entering the tombs. These can be detected on 
Quickbird imagery, yet the typical or smaller tomb entrances cannot be detected. Could 
high resolution thermal imagery detect potential buried tomb entrances? A study of the 
thermal patterns of the Valley of the Kings' tombs from space has not yet been attempted 
by archaeologists. Dense numbers of tourists and the general heat in the Valley of the 
Kings may affect this study. Other tombs would be too deeply buried beneath debris 
to be detected from space. Applying higher resolution SAR imagery may help detect 
tombs in future. 


Temples, chapels, sacred areas (architecture vs. landscapes) 

Archaeologists may debate what constitutes a religious place, as many natural areas have 
served important roles in religious practice in the past. Today, as in the past, one may be 
in the presence of their god(s) or goddess(es) by being outside. This section will deal with 
using satellite imagery to detect physical religious structures. Satellite imagery can aid 
in understanding temple orientation or building placement, as well as the relationship 
of religious structures to settlements and the rest of landscape (Fowler 1996b). Sacred 
buildings can drastically alter surrounding landscapes, especially when they are large 
in size. Areas such as Giza (i.e. the area with the most famous pyramids in Egypt) can 
be transformed from an expanse of desert to a region imbued with religious meaning. 
From space, it is far easier for archaeologists to visualize the meaning of past places, and 
see the geological formations that may have contributed to their construction. 

Satellite archaeology allows for a "virtual phenomenology," or the experience of totality 
of archaeological place. When visiting an archaeological site today, an archaeologist 
cannot fully appreciate the long-term changes through time that one can see from space. 
Satellites allow archaeologists to work back in time, to reconstruct past landscapes as 
close as possible to when religious structures might have been built. There are physical 
and metaphysical reasons for constructing religiously charged structures, and both are 
closely tied into a past culture's integration of landscape into religious belief systems. 

Remote sensing has contributed to understanding past patterns relating to religion 
and power. Using Quickbird satellite imagery, one archaeological team attempted to 
answer the question of how the people of Easter Island transported the moai over rugged 
terrain. Using composite sets of imagery Quickbird imagery from April 2003, December 
2002, and February 2003, the team pan sharpened and smoothed the imagery to find 
extensive road networks (Figure 5.15). Even in 1919, people had noted some of these 
ancient tracks, but archaeologists did not document them. The remote sensing analysis 
discovered 32 km of roads, suggesting independent groups moved the statues around 
the 17 km island. Old roads were differentiated from the modern roads during ground 



Figure 5.15 Rapa Nui (Easter Island), Chile (scale 1:130 m), the faint paths surrounding the 
volcano are the paths used to drag the stones used for the Moai, 2008 image courtesy 
of Google Earth™ Pro. 

survey. Many of the roads looked like spokes emerging from the Rano Ranaku quarry 
and were used mainly for the purpose of statue transportation. Unfortunately, many 
modern activities have destroyed the roads (Hunt and Lipo 2005). 

Archaeologists need to understand temple placement according to local cultural 
choices. In China, Feng Shui led to the placement of temples and other monu- 
ments. Knowing how Feng Shui was applied aided archaeologists in mapping the 
Tang Mausoleum in X'ian, China, over an area of 5000 km . Archaeologists used 
Landsat, SPOT, KVR-1000, and IKONOS imagery to visualize the landscape, with 
the Huiling Mausoleum visible mainly on the visible IKONOS imagery (Boehler et al. 
2004). When the orientation, size, and materials of temples are known, archaeologists 
can select the remote sensing methods and satellites most likely to detect additional 
temple or religious sites. This may work well in desert areas, such as the region of 
Tuna el-Gebel, Middle Egypt, where larger religious structures may be buried just 
beneath the ground surface (Figure 5.16). 


Centers (such as Stonehenge), inscribed landscapes 

Ceremonial features and inscribed landscapes remain closely connected to religious 
beliefs, yet are different in terms of how satellites might detect them from space. 
Ceremonial centers will be connected to trails, mountains, and other natural features, 



Figure 5.16 Tuna el-Gebel, Egypt (scale 1:178 m), note numerous buried structures just beneath 
the desert sands, which are visible from space, 2008 image courtesy of Google 
Earth™ Pro. 

with a journey often required to reach them. Total landscapes could be sacred, along 
with more regular aspects of landscapes associated with agricultural patterns (Kidder 
and Saucier 1991). SPOT imagery, digital elevation models, and GIS aided archae- 
ologists in analyzing possible ritual unity amongst Minoan peak sanctuaries dating 
to ca. 2300—1500 bc (Soetens et al. 2002). Depending on where centers are placed, 
the landscape type can be studied for the purposes of quantifying elevations, center 
size, and relationship to known settlements. For example, in the northern basin of 
the Rio Grande in Peru, a digital terrain model aided archaeologists in understanding 
geoglyph placement (Reindel et al. 2006). These geoglyphs would normally only be 
detectable on high-resolution satellite imagery. Detecting religious centers can have 
other purposes as well. Archaeological remote sensing work in Poverty Point used Land- 
sat data to detect site ridges and the main plaza area (Figure 5.17). In this case, remote 
sensing aided the archaeologists in examining technology and how it affects cultural 
interpretation and public outreach (Williamson and Warren-Findley 1991). 

How much can satellites detect? 

Coring, excavation, and subsurface archaeological detection methods remain the only 
way, at present, to gain highly detailed information about archaeological material below 
the current surface. Until more time- and cost-efficient ways of detecting archaeological 



Figure 5.17 Poverty Point, Louisiana, Landsat image in pseudocolor, image courtesy of NASA 
World Wind. 

Figure 5.18 Photograph of coring in Middle Egypt, image by Sarah Parcak. 



material appear, archaeologists will need to rely on initial, broader landscape analyses 
with remote sensing, followed by one of the aforementioned methods for more detailed 
investigation. How long would this type of coring approach take? Despite the feasibility 
of wide-scale systematic coring across the Nile floodplain (Figure 5.18) coring would 
be an excessively expensive and time-consuming method of locating buried sites. For 
example, concerning the 2450 km area of the author's fieldwork work in the East Delta 
and Middle Egypt, if one placed cores 500 m apart (looking for medium to large buried 
sites), with a full day required to sink a seven meter deep core (as experienced by the 
author) it would take 4898 days (13.4 years) to complete this survey. With excavation 
seasons in Egypt averaging two months, or 48 workdays per season, a similar floodplain 
coring survey would take 102 years. Sadly, there is neither enough funding nor trained 
personnel to carry out these studies. 

Such a project would undoubtedly reveal numerous buried archaeological sites and 
would be invaluable for landscape evolution studies, not only in Egypt, but if similar 
approaches were applied at other sites around the world. Owing to current cost and time 
constraints for such a coring survey, perhaps a smaller series of random cores should be 
placed between known surface sites until more efficient technology exists to perform 
subsurface landscape scans. Hyperspectral satellite images such as ASTER can detect 
far more subtle landscape changes than SPOT or Landsat satellite images and may aid 
in revealing potential subsurface sites. CASI and LIDAR hold great promise for the 
future of revealing slightly buried or obscured archaeological remains. Understanding 
the vagaries of each landscape and feature type will help archaeological teams to plan 
for their remote sensing projects to gain the best results possible, no matter what the 
project goals or constraints. 




Case studies are invaluable for illustrating how specific archaeological projects have 
implemented satellite remote sensing work in reaction to specific regional issues. This 
chapter features six remote sensing and survey projects in different regions that con- 
tain broad landscape types and diverse archaeological features. These examples appeared 
appropriate owing to their in-depth approaches to archaeological remote sensing and 
their placement of satellite archaeology in broader archaeological contexts. By discussing 
the different approaches each project took, this section will explore how and why each 
study was successful in its specific context. The studies occur in regions where many 
remote sensing studies are already taking place and include The Peten (Guatemala), 
Angkor Wat (Cambodia), Xi'an (China), Ubar (Oman), Horns (Syria), The Delta and 
Tell-el-Amarna (Egypt). Three of the studies chosen represent regional remote sensing 
studies in the Middle East. Ubar is representative of desert remote sensing studies. In 
the cases of Egypt and Syria, one might initially consider them to yield similar flood- 
plain environments, yet each region is ultimately distinct and hence require different 
approaches. Many ancient cities emerged in floodplains, and with a long history of 
regional archaeology in the Ancient Near East, an examination of Horns, Syria, empha- 
sizes approaches to studying a single multi-period site in a broader regional content. 
In Egypt, the case study is broader in scale and compares and contrasts how settlement 
pattern studies can be conducted in different floodplain regions of the same river sys- 
tem. It is only through generating a detailed discussion of broader projects that other 
archaeologists can gain insights into how they might model their own remote sensing 
survey projects. 

The Peten, Guatemala 

Features on ground in rainforest regions across the world, such as the Peten, Guatemala, 
may be difficult or virtually impossible to see let alone traverse. This leaves satellite 
remote sensing as one of the only viable tools for locating previously unknown sites and 
features. Such limitations and difficulties have encouraged archaeological remote sensing 
work in the Peten region of Guatemala (Figure 6.1) by William Saturno and his team. 
Satellite remote sensing allowed a much better understanding of the region surrounding 
one of the most important Mayan archaeological sites, San Bartolo. A pyramidal structure 
at San Bartolo contained painted decoration that represented the oldest known Maya 



Figure 6.1 Tikal, Guatemala: (a) ASTER imagery NDVI; (b) Landsat NDVI (scale 1:2393 m), 
images courtesy of NASA. 

hieroglyphic writings. The remains dated to the late Preclassic period (400 bc— ad 200), 
and more specifically, 200-300 bc (Saturno et al. 2006). 

Saturno and his team faced several problems in their efforts to gain a better under- 
standing of San Bartolo in its regional context. First, the Peten, Guatemala measures 
some 36,000 km , covered in sense vegetations, and represents a vast undertaking 
for any survey work. Second, half of the region had been deforested since the 1970s, 
destroying many archaeological sites as they are exposed to looters and unscrupulous 
developers. Third, the remaining area is threatened by a lack of conservation measures 
and could disappear in the next 10—20 years if they are not implimented, making 
satellite remote sensing absolutely crucial for discovering and recording archaeologi- 
cal data shown in Figure 6.2. This potential broad scale loss has major implications 
for Mayan archaeology as there are numerous archaeological sites remaining to be dis- 
covered in the dense forest regions. If Saturno s work at one site alone (San Bartolo) 
is any indication of the potential richness of undiscovered sites, then the archaeo- 
logical sites awaiting future discovery in the Peten could prove to be just as, if not 
more, important. 

For more than 170 years, archaeological surveys have proven to be nearly impossible 
in the dense and virtually impassible Central American rainforests. Through a Space 
Act Agreement, in essence an accord allowing the sharing of satellite datasets, the 
University of New Hampshire and NASA's Marshall Space Flight Center negotiated a 
joint effort to implement a settlement survey in the Maya lowlands. The archaeological 



/ w 







> - 

• -^ 


Figure 6.2 The Peten, Guatemala, Landsat (scale 1:41700 m), image courtesy of NASA. 

goals included identifying ritual, economic, demographic, and environmental factors 
in the Preclassic environment surrounding San Bartolo. NASA's objectives focused on 
studying settlement patterns via hydrology, examining the regional geology, locating 
bajos, and linear features, and mapping drainage patterns. NASA also wished to create 
vegetation maps to analyze subsistence strategies, and generate climate models of the 
region (Saturno et al. 2007). 

By using a combination of satellite data, particularly Landsat TM, IKONOS, Quick- 
bird, Urban STAR- 3 (an X-band system used for reflecting surface topography), and 
AIRSAR, the project succeeded in locating archaeological sites, roadways, canals, reser- 
voirs, and bajos. Although the remote sensing analysis identified archaeological sites, 
specific spectral signatures have not yet been isolated. In some cases, such as Lindbergh's 
early surveys, sites have been found in the northeast Yucatan. However, the dense vege- 
tation remains an obstacle to most survey work (Figure 6.3). Tom Sever 's surveys in the 
mid-1980s adopted supervised and unsupervised classifications for Landsat TM imagery, 
as well as TIMS and infrared (IR) photography, but found no correlation between veg- 
etation patterns and archaeological sites. Instead, IKONOS imagery proved to be the 
best archaeological site indicator when pan sharpened and passed through a Brovey 
transform. Here, vegetation signatures were correlated during ground navigation to the 
unknown sites. On the ground, the survey verified past human activity, recorded tem- 
ples, causeways, reservoirs, and agricultural areas. The project incorporated all the data 
into a GIS for further analysis (Saturno et al. 2007). 



Figure 6.3 Tikal, Guatemala (scale 1:2393 m), 2008 image courtesy of Google Earth™ Pro. 

These archaeological sites appear to be creating a microenvironment, with chemical 
leeching in the soils from the limestone masonry and limestone plasters. Vegetation is 
clearly being affected by the underlying archaeological structure, although the exact 
mechanisms are not completely understood and further analysis is needed. Some ques- 
tions requiring further assessment include: Is there an optimum month in which a 
spectral signature will be reflected most strongly? Which different remote sensing meth- 
ods will work best in detecting the sites? The on-going project is applicable for other 
parts of Central America, especially the Peten and northern Belize regions where the 
bulk of excavation work in Maya archaeology is currently concentrated. Saturno's inves- 
tigations have the potential to save other research projects a great deal of time and money, 
not just in Central America, but worldwide where rainforests obscure ancient settlement 
sites and features (Saturno et al. 2007). Archaeological remote sensing analysis incorpo- 
rates site and feature detection as a first step to answering larger ecological questions, 
which Saturno, Tom Sever, and others have already demonstrated (Figure 6.4). 

A further line of inquiry focuses on whether the Maya used the bajo islands (seasonally 
flooded swamps) for agriculture? This issue is debated among Maya archaeologists. Errin 
Weller (2006) notes that bajo islands make up 40 percent of Guatemala's landscape, mak- 
ing them likely candidates for at least some agricultural usage. Weller used Landsat, SAR, 
SRTM, and IKONOS satellite data to examine bajo islands surrounding the well-known 
archaeological sites of Tikal and Yaxha, more specifically Bajo La Justa and Bajo Santa Fe. 
In satellite data analysis, band combinations 4-3-1 and 4-3-2 showed a yellow signature 
that might be connected with the decay of limestone. Principal components analysis 






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Figure 6.4 The Peten, Guatemala, Landsat pseudocolor (scale 1:250 m), image courtesy of NASA 
World Wind. 

(PC A) found additional bajo islands, while combining the IKONOS, Landsat, and SRTM 
data showed a number of larger bajos. Edge detection techniques, however, did not man- 
age to isolate any features, which had previously enhanced canals and causeways. In Sante 
Fe, 87 bajos appeared with 209 water features. Weller argued that the bajos should to 
be considered as communities, either for agriculture or settlement (Weller 2006). The 
debate continues as research proceeds, but at this stage the data illustrate just how 
important environmental factors are in affecting past communities in Central America. 

Angkor Wat, Cambodia 

Angkor Wat is one of the best-known monumental archaeological sites in Southeast 
Asia, occupied from the 9— 15th centuries ad. It is the largest and most elaborate urban 
complex in the pre-indus trial world, and courts many thousands of tourists each year 
(Figure 6.5). Conversely, its fame makes it one of the most targeted sites for looting in 
Southeast Asia — particularly during the recent civil war, with the antiquities market 
financing the purchase of weaponry. Satellite remote sensing, however, has contributed 
to a better archaeological understanding of Angkor Wat, and has enabled archaeologists 
to collaborate with the Cambodian government to create better site management maps. 
Satellites imagery analysis has provided surprising insights into past water management 
strategies at Angkor, and revealed a much broader urban complex around the temple 



Figure 6.5 Angkor Wat, Cambodia, Landsat image (scale 1:250 m), image courtesy of NASA 
World Wind. 

area, which now demonstrates that in size and complexity Angkor Wat far exceeds 
previous estimates (Figure 6.6). 

Modern archaeological investigations began at Angkor Wat with the use of RADAR 
data and aerial photography (Moore and Freeman 1997, Moore et al. 1998), and 3D 
modeling using aerial photography (Sonneman et al. 2006), which elucidated traces of 
a complex network of artificial water systems at the site and throughout its hinterland 
(Moore 1989). SIR-C data traced ancient fields, canals, and reservoirs that had remained 
otherwise unidentified (James 1995). AIRSAR and TOPSAR data revealed barays, a 
form of artificial basin, demonstrating the continued importance of more-aerial based 
remote sensing (Freeman et al. 1999). This has led to additional AIRSAR scanning of 
Angkor, which promises to revolutionize further how archaeologists viewed this site and 
its hinterland. 

In September of 2000, AIRSAR scanned an area of 3000 km surrounding Angkor, 
and revealed that the main core covered an area measuring 1000 km , while the temple 
complex spread out over an area of 2000 km . The latter area also contained local shrines, 
and showed that associated settlements generally lay in low density regions. The archae- 
ological project in Cambodia was a collaboration between the French government, the 
Australian Government, the World Monuments Fund, the Mekong River Commission, 
and NASA's Jet Propulsion Laboratory, all of whom recognized the need for an integrative 
approach to site conservation, management, and public presentation. Although AIRSAR 
did not actually reveal any previously unknown monuments, it allowed for a holistic 
view of the total area and showed changes through time (Fletcher and Evans 2002). 

It is noteworthy that the AIRSAR imagery allowed archaeologists to see through 
98 percent cloud cover, surpassing the capabilities of conventional satellite imagery. 



Figure 6.6 Angkor Wat, Cambodia, Landsat pseudocolor image (scale 1:250 m), image courtesy 
of NASA World Wind. 

Ultralight aircraft also helped take lower elevation aerial photographs, which were 
especially helpful for inaccessible parts of the site that were dotted with anti-personnel 
mines. The overall project results revealed distinct anthropogenic changes to the land- 
scape reservoir, showing that the water system incorporated an interconnecting system 
of channels and soil embankments (Figure 6.7). The ancient ponds offered a sufficiently 
efficient agricultural system that the Cambodian people are reusing them today (Evans 
etal. 2007). Additional ground systems emerged in three-band color composite imagery, 
which illuminated the overall urban layout and ancient forest clearance to accommodate 
the city's hydrological network. Today, satellite imagery also reveals that, erosion and 
flooding pose major threats to the ancient city. Hence, remote sensing analysis will also 
aid in monitoring and mitigating threats to Angkor as a cultural resource (Fletcher and 
Evans 2002). 

The work at Angkor has progressed to form part of the Greater Angkor Project 
(GAP), combining AIRSAR data with SIR-C/X-SAR data. Research reveals that, orig- 
inally, artificial moats and dykes allowed Angkor to develop into a great city (Pottier 
2002). Once the populace controlled land and water resources, which formed the roots of 
Khmer culture, wealth began to accumulate. The construction of temples and affiliated 
religious structures coincided with the integral establishments of supporting water con- 
trol systems. Although the floodplain had many advantages, being particularly fertile 
and good for cultivation, it was vastly altered and augmented to serve the people of 
Angkor. Further analysis, focusing on the C, L, and P bands in AIRSAR, in conjunction 
with old maps and aerial photographs, examined a 1500 km mosaic, finding surface 
variations relating to moisture and vegetation. Hence, archaeologists have determined 
that the Khmer culture was intimately familiar with the hydraulic cycles on a local scale. 



Figure 6.7 Angkor Wat, Cambodia, note high resolution of temple complex (scale 1:491 m), 
2008 image courtesy of Google Earth™ Pro. 

They understood the full importance of controlling water sources in the rainy season ver- 
sus the dry season through significant terrain alteration. Remote sensing has also allowed 
archaeologists to understand better how the Khmer defined their political organization 
(via landscape use and settlement patterns), and has redefined our understanding of 
power and place in a Khmer setting. Furthermore, it has clarified the role of multiple 
sites in relation to and in isolation from Angkor (Moore et al. 2007). 

Angkor Wat's isolation and location in a densely vegetated rainforest area has partly 
protected it and partially endangered it from looters, while various revolutionary groups 
have disrupted the tourist industry through kidnappings and massacres. In such danger- 
ous and difficult circumstances, satellites have emerged as invaluable aids in modeling 
Angkor Wat to design and implement safer management strategies. Many of the same 
techniques that archaeologists have used successfully to locate archeological sites in 
Central America should be able to locate previously unknown sites in other parts of 
Cambodia. However, two major problems emerged, namely cloud cover and standing 
water. Although there are cloudless days over Angkor and elsewhere in Cambodia, like all 
rainforests, the prevalence of clouds can make finding cloud-free satellite imagery (out- 
side of AIRS AR) much harder. It remains to be seen what sensors, such as LIDAR, reveal 
about the landscapes at Angkor, as they can be flown on cloudless days or even in the 
space of a few cloudless hours. Hence, LIDAR and AIRSAR could allow archaeologists 
to review the Cambodian landscape more frequently and with greater affect: Perhaps 
more reservoirs, temples, and settlement sites remain to be found, and indeed, protected 
for future generations of archaeologists, tourists, and the Cambodians themselves. 



Figure 6.8 Xi'an, China, Landsat image (scale 1:574 m), 2008 image courtesy of NASA 
World Wind. 

Xi'an, China 

In China, aside from the Great Wall, there is no archaeological site or feature more famous 
than the terracotta warriors of Xi'an (Figure 6.8). Thousands of life-sized models of 
soldiers compose the guard for the tomb of Emperor Qinshihuang, who lived ca. 259—210 
bc, and who unified China in 221 bc. Each solder displays individual hair styles, facial 
features, coloring (which does not last when exposed to air), and detailed equipment and 
uniforms related to their rank and function. The power and wealth Emperor Qinshihuang 
held to command the creation of such an army was not inconsiderable. With more 
than 8000 terracotta horses and over 40,000 bronze weapons known from pits to the 
east of the Mausoleum, Xi'an attracts hundreds of thousands of visitors each year. The 
beautiful bronze armor and wagons, popularly reconstructed in the Hollywood movie 
Hero, attest to the high level of craftsmanship during Emperor Qinshihuang 's reign 
(Wenlie 1998). 

One of the main questions that archaeologists have attempted to answer using satellite 
remote sensing analysis is whether or not the tomb complex of Qinshihuang is still mostly 
intact. According to ancient sources, the "tomb" complex contained nothing less than 
a complete underground city, replete with a river of "flowing mercury," and held many 
precious and semiprecious jewels. Tracing the entire complex would likely represent 
the most significant archaeological "find" in the history of archaeology, surpassing the 
1922 discovery of Tutankhamen's tomb by Howard Carter. Unlike the uncovering of 
Tutankhamen's tomb, this find is vastly larger and is much more slowly revealing its 
secrets; archaeologists still only have a general idea of what it is meant to contain. The 
biggest remaining question is to what extent is it preserved, having been intended to 
exist as a model universe for the Emperor (Rawson 1996). 



The question of the potential looting of Qinshihuang's tomb though can be gauged by 
monitoring artifacts appearing on the illegal antiquities market. If looters had robbed 
the tomb in the past two centuries, one would assume that similar funerary pieces 
would already have appeared in museum collections or private published collections. 
China has only been open to foreign tourists for the past 30 years, so most looting 
would likely have occurred at a local level. The significance of the area was not even 
known to the local population until 1974, when the Chinese government intensified 
archaeological excavations in the area. The whole complex remains closely guarded due 
to its importance to China's tourism industry, and also the role it plays in Chinese 
national pride. The Shaanxi Provincial Cultural Relics Bureau and the Management 
of Qinshihuang Mausoleum Museum manage the site; the latter takes responsibility 
for excavating and publishing site data. Fortunately, the Lintong District Government 
has banned construction projects within the protection zone around the Mausoleum, 
described in an annual report to UNESCO (Anon. 2003). 

The use of satellite remote sensing has already aided archaeologists in understanding 
the construction and state of preservation of the Qinshihuang Mausoleum (Xiaohu 
2004). It should be noted that, in comparison to the other case studies, relatively few 
publications exist in English discussing the application of satellite remote sensing to this 
region. Combining the Mausoleum and the adjacent terracotta army pits (the densest 
archaeological area) gives a total area of 1 9-63 km , or an area large enough to be analyzed 
coarser resolution satellite data. The tomb itself measures 1014 m east— west by 2228 m 
north— south in an oblong shape, and rises to 65 m tall, in a total area of 2.26 km 
(Anon. 2003). The landscape surrounding the site is not even, and combines farmland 
and urban structures. 

Archaeologists have applied a number of satellite and remote sensing techniques to 
analyze the Qinshihuang Mausoleum, including airborne and geophysical applications. 
The study allowed comparisons between aerial photographs from 1956 and 1974 to assess 
general landscape changes. Prior to 2005, archaeologists had not conducted any hyper- 
spectral studies on Chinese archaeological sites. An OMIS2 spectrometer analyzed a 60 
km area, and carried out both daytime and nighttime scanning, as well as recording gen- 
eral temperatures, humidity, and spectrum measurements of the ground surface. Stone 
stays relatively cool during the day, but becomes warmer by nightfall due to thermal 
inertia, or the absorption of heat. This thermal inertia is detected by remote sensing anal- 
ysis. Hence, the slopes of the Mausoleum facing the sun would absorb more energy since 
variations in topography and surface angles to the sun mean differential heating. It was 
hoped that this would reveal additional information about the Mausoleum's architecture. 

For example, a higher temperature was indeed connected to the 21 million m of 
rammed earth composing the Mausoleum, since more compact soil has better conduc- 
tivity. In general, trees blossomed better on the archaeological site, and were better able 
to resist freezing due to the conducted geothermal energy, while reduced crop growth 
was connected with underlying ash pits at the site. Some soil removal was observed 
beneath the Mausoleum, but archaeologists calculated that the preserved tunnel was 
not deep enough to breech the underground palace complex that had been observed 
via "thermal anomalies." Remote sensing has also observed eastern and western tomb 
entrances. In order to assess this complex and its hinterlands, archaeologists created 
a digital terrain model for a 3 km radius surrounding the tomb (Figure 6.9) (Kelong 
etal. 2004; Tan et al. 2006). 



Figure 6.9 Xi'an, China (scale 1:168 m), note distortion in imagery, 2008 image courtesy of 
Google Earth™ Pro. 

This author's conversations with Chinese archaeologists have revealed that the 
Mausoleum is unlikely to be opened any time in the near future. Chinese archaeolo- 
gists assert that current archaeological methods are insufficiently advanced to handle 
the most important tomb in China, and wish to excavate it using best techniques 
possible. They are worried about preservation, which the present work on the terra- 
cotta warriors has proven to be problematic. Hence, the true extent of the tomb awaits 
more detailed probes and more advanced 3D subsurface scanning. Perhaps subsurface 
scanning will become more widely available and will advance to the point where archae- 
ologists can map an entire site without excavation. Cutting tiny boreholes enables 
limited viewing to retrieve information about the surviving material culture within 
the tomb. 

In the meantime, many more questions exist about the extent of the surrounding army. 
How much have the buried terracotta warriors affected spectral signatures inherent in 
surrounding soils and vegetation, as seen in Figure 6.10? What type of satellite sensors 
may be able to detect the buried remains outside of hyperspectral data? One wonders if 
Quickbird or IKONOS imagery would be able to detect the vegetation differences in 
their IR bands. The seasonality of the optimum satellite imagery is another issue that 
needs to be studied for this region in particular. Perhaps there are additional associated 
terracotta warrior groups that are waiting to be discovered. Hence, the current and 
developing satellite technology need not wait for a farmer to dig a well to discover 



Figure 6.10 Xi'an, China, Landsat pseudocolor (scale 1:168 m), note different reflectance when 
compared to Figure 6.8, image courtesy of NASA World Wind. 

the complex. Having already detailed the Mausoleum, the Chinese government has 
already begun to protect and preserve China's most important archaeological site. 

Ubar, Oman 

One of the more "romantic" remote sensing studies uncovered the famed city of Ubar 
in the so-called Arabian empty quarter of Oman (Figure 6.11). Ubar formed a key 
component in a series of desert caravanserai (or a roadside rest house), facilitating trade 
in frankincense and myrrh, perhaps as early as ca. 2000 bc and continuing until ad 
200. The general region around Ubar was mentioned in the writings of Ptolemy, the 
Koran, and the Thousand-and-One Arabian Nights. Pliny noted that there were "octoman- 
siones" (octagonal-shaped stations) along the frankincense route, providing an invaluable 
architectural and aerial clue. Ubar's legendary placement in a vast desert, however, 
made it virtually impossible to relocate using more conventional remote sensing studies 
(Welzenbach 1995; Clapp 1996; Fisher and Fisher 1999). 

In 1930, the British explorer Bertram Thomas mentioned a track of some sort, which 
was rediscovered in the 1950s. Problems with dunes, however, made traversing this road 
too difficult. Archaeologists and explorers contacted NASA's Jet Propulsion laboratory 
after reading about the results of a SIR-A mission in Egypt, and thought a similar 
approach might aid in re-discovering Ubar. With more data scheduled to be collected 
from SIR-B in 1984, they wanted to know if it was possible to have imagery taken 
over the "empty quarter." This proved to be possible. The team obtained Landsat TM, 
illustrated in Figure 6.12, SPOT 10 m data, as well as SIR-C imagery for analysis before 
conducting their ground- truthing and potential excavation seasons. 



Figure 6.11 Ubar, Oman (scale 1:8608 m), 2008 image courtesy of Google Earth™ Pro. 

Figure 6.12 Ubar, Oman, Landsat image (scale 1 : 8608 m), image courtesy of NASA World Wind. 



Many remote sensing methods were used in the analysis. Edge detection filters proved 
to be the most useful on the SIR-C data, which found a series of older tracks in the area 
Ptolemy called the "Omanum Empirium." Other methods attempted included band 
combinations, contrast enhancements, spatial filtering, edge enhancement, and Landsat 
band ratio composites. Principal components analysis (PC A) also aided in detecting 
potential tracks beneath the dunes, while direct band ratioing, helped the team to 
differentiate between ancient and modern tracks. During the analysis, an L-shaped 
feature appeared, which seemed somewhat narrow. The team used a helicopter to reach 
the site, which proved to be a natural shallow depression and the site of a major Neolithic 

In general, the remote sensing analysis allowed this project to eliminate large areas 
from their ground survey, and provided small areas for visual assessment before conduct- 
ing surface reconnaissance. An archaeological surface team followed the paths found via 
the filtered SIR-C data, and found a potential candidate for the location of Ubar near the 
modern village of Shisr. Excavation over three seasons yielded a number of potsherds, oil 
lamps, and Iron Age material from Rome, India, Persia, and Greece, dating to between 
800 bc— ad 400. Excavation also produced a possible towered fortress, which the team 
connected to Pliny's "octomansiones" (Blom et al. 1997, 2000, 2007). Circumstantial 
evidence suggests that this site represents the city of Ubar, but other considerations need 
to be assessed. Perhaps "Ubar" did not represent a single site, but was instead a region, or 
series of places caravanserai could meet for trade. Chemical analyses (i.e. residue analysis) 
on the pottery and other areas might have aided the team in connecting the site to the 
frankincense trade. This site, however, was rather small, and has some difficulty fulfilling 
all of the requirements of fabled Ubar, including an inability to support large numbers 
of people due to minimal water sources. In other regards, however, the project proved 
the successes of applying RADAR data in desert regions, documenting the ability of 
satellite remote sensing to locate sites using good scientific and historical data. 

Horns, Syria 

The modern Middle East includes the broad area composing the ancient Near East 
(which included mostly Egypt, Turkey, Syria-Palestine, Iraq, and Iran) and is an impor- 
tant region in which many surveys and remote sensing studies have taken place, or 
are currently ongoing (McAdams 1981; Wilkinson 2001, 2002; Alizadeh et al. 2004; 
Wilkinson et al. 2004). It is necessary to discuss a number of modern landscape analy- 
sis issues before introducing the case study about Horns in Syria. Throughout the area 
composing the ancient Near East, modern agriculture has often obscured past remains, 
especially deep plowing that has destroyed many ancient levees and low-lying sites. 
Scholars have also noted a diversity of terrain, which can obscure sites. Additionally, 
different vegetation types may mask sites in various ways. The issue of site visibility on 
the ground also makes aerial reconnaissance necessary (Banning 1996). Other impor- 
tant features, however, may be hard to identify from space. The locations of levees and 
settlements have a long history in the ancient Near East, and have changed greatly 
over time, leaving a complicated network of former river-channels and sites throughout 
Mesopotamia. Satellite imagery and SRTM data have helped to link levee usage with 
adjacent archaeological sites, as well as including distinguishing past sequences of levees 
from different time periods (Hritz and Wilkinson 2006). 



Figure 6.13 Horns, Syria, Landsat image (scale 1:22812 m), note how ancient water systems are 
visible even on coarse resolution imagery, image courtesy of NASA World Wind. 

Many landscape types occur across the modern Middle East, spanning deserts, 
scrubland, floodplains, lowlands, mountainous terrain, coastlines, and submerged past 
landscapes (Figure 6.13). Many remote sensing projects have advocated a variety of 
approaches for the study of these current and past landscapes, while in particular desert 
environments require more intricate procedures regarding landscape analysis issues. 
In southern Arabia, Landsat data has yielded visible remains from ancient oases, espe- 
cially in Yemen, where some larger buildings and older field systems survive (Brunner 
1997). Landscape changes present additional challenges. At the archaeological site of 
al-Raqqua in north central Syria, archaeologists compared Landsat TM, SPOT, and 
Corona imagery scanned at 1200 and 4000 dpi. The site is known to have settlements 
from the Neolithic onwards, with 20 identified structural complexes, and was aban- 
doned in ad 1250 until the 1940s. However, rapid urbanization has occurred here 
since the 1960s. The study noted a marked difference in the four types of satel- 
lites imagery: Corona data revealed medieval river channels and past meanderings 
across the floodplain. Corona imagery also detected that 45 percent of landscape had 
been unaffected by urbanization (Challis et al. 2002). In addition, Corona imagery 
detected the shrinking of Lake Qatina in Syria due to modern agricultural expansions 
(Philip, Jabar et al 2002). 

Modernization has affected landscape changes and satellite image analysis in different 
ways. For instance, dam construction and reservoirs in the Middle East make it an 
urgent priority to conduct archaeological surveys in areas that will soon be submerged. 
A satellite remote sensing survey in Turkey focused along the river course behind the 
construction of the Birecik Dam, which was completed in 2000, close to the border with 
Syria. In this circumstance, SPOT, Corona, Landsat, and KVR-1000 satellite images were 



used instead of aerial photography, as permission was not granted by the military to use 
aircraft in this broader sense. The analysis and work took place before the rising waters 
could cover a number of sites. Large-scale maps were created, allowing the researchers to 
map ancient quarries, past roads, and mounds (mainly via Corona imagery). Problems 
were noted with erosion and deposition, as well as deep plowing, which had scattered 
material culture from small sites (Comfort 1997a, 1998, 1999; Comfort et al. 2000; 
Comfort and Ergec 2001). 

Other ancient features altered by modernization may appear only when viewed from 
space. When examining ancient settlement development in the North Jazeira, specialists 
noted hollow ways (or ancient spoke-like roads emerging from ancient sites, visible 
from space as slight depressions) becoming visible in both aerial photographs and high- 
resolution satellite images, with the subsequent collection of past ceramic material 
yielding a 500-year span in this area (Emberling 1996). Archaeological sites may also 
appear quite unexpectedly. Using Landsat ETM+ and ASTER imagery, an archaeological 
team detected what was postulated to be a late Holocene meteorite impact crater, known 
as the "Umm al-Binni structure" in the marshlands of southern Iraq. This appeared only 
when the marshlands were drained, and may be connected to larger issues related to 
global climate change around ca. 2200 bc (Master and Woldai 2004). 

In the floodplains of the Middle East archaeologists can utilize satellite imagery 
to map what are known as "signature landscapes," or landscapes dating to a specific 
period of time. Corona imagery revealed Sassanian period (ca. ad 224—642) landscapes 
in northwestern Iran, in the region of the Mughan Steppes (Alizadeh and Ur 2007). 
The study synthesized data from the satellite imagery and ground survey, and identified 
transportation routes, nucleated settlements, and irrigation areas. Some areas contained 
settlement resources or features to encourage resettlement, while other areas did not. 
In general, a palimpsest model has been advocated (Wilkinson 2003), as settlements 
wax and wane, being affected by cyclical weather patterns and larger environmental 
changes. In contrast, nomadic landscapes were found to be difficult to locate, but could 
be traced by Corona imagery, which also revealed fortified settlement complexes. In 
areas of low undulating topography, small scatters of material culture survived, but 
most of these clusters were found to be more modern rather than ancient (Alizadeh and 
Ur 2007). 

Having illustrated various approaches to Middle Eastern landscapes, this section 
will focus more heavily on the work taking place at the site of Horns, Syria. This 
project used a combination of Corona, Landsat (Figure 6.14), IKONOS, and Quickbird 
imagery to assess the landscape broadly and to examine long-term human— environment 
interactions. Archaeological sites appeared as tells with low relief soil marks, with 
remains ranging from small walls less than 1 m wide to large multi-period settle- 
ments. In general, Landsat imagery proved useful for environment characterization and 
for visualizing rates of change, but as not good for site detection and mapping due to its 
coarser resolution in comparison to IKONOS and Quickbird. Like all remote sensing 
projects, teams must consider the size of the features they hope to detect as well as their 
associated landscape features. The Horns project, at its start, had no database of remains, 
or any aerial photography coverage in its 630 km study area and needed a site target- 
ing system. By using visual bands on Landsat, the project compared how Landsat and 
Corona detected ancient remains. Ancient field systems, seen on Figure 6.15, appeared 
on the Corona imagery. In this case, visual detection aided the team more than any 





Figure 6.14 Horns, Syria, Landsat pseudocolor (scale 1 :4240 m), image courtesy of NASA World 

Figure 6.15 Horns, Syria (scale 1:2281 m), note cropmarks in the fields showing older water 
courses, 2008 image courtesy of Google Earth™ Pro. 



detailed processing of Landsat imagery data due to the visually changed landscapes 
(Donoghue et al. 2002; Philip, Beck and Donoghue 2002). 

Corona imagery detected non-multi-period sites otherwise not visible in the IKONOS 
imagery. The study compared site detection techniques by examining the relation- 
ships between ground moisture, grain size of the soil, soil iron oxide content, geology, 
vegetation, and image and absorption reflection factors (Wilkinson et al. 2006). Corona 
imagery taken in the minimum and maximum crop growth periods also contributed to 
site visibility. The Corona imagery proved somewhat problematic for the analysis, how- 
ever, as the team encountered difficulties finding landmarks that correlated with recog- 
nizable coordinates today, thus making georeferencing somewhat inaccurate. Overall, the 
team noted a high density of features, yet subsequent landscape modification was a major 
problem for the team. For example, some sites had been visible in the IKONOS imagery 
but did not appear clearly on the ground owing to more recent extensive plowing. 

The team relied mainly on visual interpretation of the satellite imagery, as they 
concluded that it would not be possible to create a number of spectral signatures that 
would work in their study environment. They emphasized the need for further remote 
sensing research in areas with similar conditions. The IKONOS imagery produced 
an error rate of greater than 25 m. The team compiled ground control points using 
a handheld GPS, but found it difficult to identify archaeological sites that had been 
cut into and then backfilled with the same material. Regarding their experiences, team 
cautioned future researchers about limitations of satellite imagery applications for survey 
work (Beck et al. 2007). 

One wonders what results might have been obtained using multispectral analysis. For 
instance, more positive results might emerge from material culture being examined in 
similar environments, such as floodplains. Tells are broadly similar in form and general 
composition across the ancient Near East, especially in areas where mudbrick structures 
dominate. Similar remote sensing methods may work in identifying tells in different 
parts of the world in related landscape types such as floodplains. Although spectral 
signatures for sites may vary due to differences in mudbricks, ancient technologies, pot- 
sherds, and soil matrixes, many tells will likely absorb higher amounts of water than 
their adjacent landscapes and will have higher organic debris contents. It is crucial to 
recognize what differences and similarities occur in the analysis of satellite imagery for 
archaeological site discovery, but one should also try to apply a full range of available, 
albeit continuously emerging, analytical techniques. Spectral reflectance charts may aid 
in analyzing other imagery types such as ASTER. Multispectral analysis may detect vary- 
ing crop types growing over archaeological sites, which may, in-turn, indicate specific 
site types (e.g. an occupation mound versus an overgrown fortress). Such destructions 
might be identified though normalized difference vegetation indexes (NDVI), PCA, 
classification, band ratioing, or other techniques. Soil identification might be another 
way to detect past areas of settlement. The Horns projects team did note that marl clays 
could be better identified through histogram manipulations, but this is only one of 
many other potentially successful remote sensing techniques. 

The Horns projects also encountered cost limitations. They mention spending of 
US$44,000 on IKONOS imagery for a total area of 650 km 2 , at a cost of US$68 per 
km . For future work, it might be more cost effective to purchase Quickbird imagery. 
Today, even purchasing corrected IR imagery from Digital Globe via tasking satellites, 
namely, obtaining new imagery, would cost US$36.00 per km , half the project cost 



and with a slightly better resolution. Despite this project's limited application of and 
success with satellite image data analysis, it demonstrated how valuable visual detection 
is for broad-scale landscape analysis and for ground- truthing in multiple terrains. Given 
their impressive success using visual detection, it is not hard to imagine what results 
multispectral analysis will yield for broad-scale archaeological feature detection. 

In the Middle East, predictive modeling for ancient sites is still problematic. Archae- 
ologists have noted that there is no statistical advantage to using regularly spaced spatial 
units for landscape analysis, as there are problems with spatial autocorrelation (Hodder 
and Orton 1976: 174—83). Looking for anomalies, or "noise," on images via visual 
detection or spectral signature analysis is far more likely to lead to archaeological site 
identification on the ground than randomly picking places. However, potential and 
real ground-based problems must be recognized first. Archeological features, such as 
field systems, walls, terraces, tracks, and tumuli, cluster in variable ways around vari- 
ous archaeological sites. How to identify different field patterns needs more refinement, 
especially in areas where there has been reuse: Aerial archaeology may aid in further 
reconstructing field systems. Undulating and difficult terrain hampers effective survey 
work, and creates obstacles in collecting data in a meaningful way and connecting it to 
the larger archaeological picture. However, these are problems that archaeologists are 
overcoming through the ongoing testing of multiple remote sensing methods. 

The Delta and Middle Egypt 

Ancient Egypt represents one of the best-known and most productive archaeological 
landscapes across the globe, yet regional surveys represent a fairly insignificant part 
of Egyptological research in the past 200 years (Kessler 1981; Kemp and Garfi 1993; 
Jeffreys and Tavares 1994; Jeffreys and Tavares 2001; Jeffreys 2003). Intrasite survey is 
another issue: Subsurface archaeological detection has become increasingly important 
over the past few decades (Herbich 2003; Mumford et al. 2003), detecting a number 
of mortuary and culture features that many projects have subsequently excavated. A 
few broader remote sensing projects have concentrated around the pyramid region near 
Cairo (Yoshimura et al. 1997). One such mission utilized Quickbird satellite imagery 
to map the Abusir pyramid fields, focusing on the general position and topology of the 
areas. This study found that the color of buried limestone blocks was easier to detect on 
Quickbird imagery, and encouraged combining surface detection, magnetometer survey 
and remote sensing analysis (Barta and Bruna 2005). Another desert-based study used 
Landsat and ASTER imagery to create digital elevation models and to map early to 
mid-Holocene landscapes in the Western desert, when conditions were more humid. 
Using topographic, hydrological, and geological information, the study predicted areas 
where Holocene sites were most likely to be detected along ancient water sources. These 
potential target sites were then surveyed to survey the existence of past human activity 
(Bubenzer and Bolten 2003; Bolten et al. 2006; Bubenzer and Riemer 2007). 

Egypt's heritage faces many threats in the face of a rapidly increasing population, 
urbanization, and looting (see Chapter 8), which is a problem faced elsewhere across 
the Middle East (Schipper 2005), and the world as a whole. Decorated and inscribed 
wall faces are currently flaking off temples in Luxor due to rising salinity levels 
(Brand 2001a, 2001b), while agricultural developments have harmed and continue 
to threaten archaeological sites. When Napoleon visited Egypt in the early 1800s, as 



illustrated in The Description of Egypt (Jomard 1829), Egypt's floodplain landscapes were 
covered with ancient mounds, or "tell" sites (Edwards 1891). In the early 1900s, archae- 
ologists had already noted the widespread destruction of these mounds, which farmers 
had begun to mine for fertile Nile silt and mud, known as sebbakh (Kelsey 1927). Egypt's 
landscape continues to change: Egyptian farmers are reclaiming desert lands for agricul- 
ture (Giddy and Jeffreys 1992), while growing settlements on or beside archaeological 
sites also threaten to engulf and destroy many archaeological sites. 

Regional archaeological surveys form the only way to detect the surviving surface 
sites before they too are lost or engulfed. In addition, landscape geomorphology issues 
can best be understood from a spatially based perspective (Coutellier and Stanley 1987; 
Said 1988, 1990; Hassan 1997). With so many new discoveries being made on known 
archaeological sites, changing the way Egyptologists frame the history of ancient Egypt, 
it remains to be seen what regional settlement patterns can contribute via the subfield 
of settlement pattern studies, which is otherwise virtually non-existent in Egyptian 
archaeology. Settlement archaeology has become increasingly important in Egyptol- 
ogy (Kemp 1972) while the diverse landscapes composing Egypt make it necessary 
to approach settlement pattern studies using satellite archaeology. This author's study 
area in the East Delta was chosen initially to investigate the regional settlements sur- 
rounding Tell Tebilla (Mumford 2002, 2003), where the author had spent several years 
excavating under the auspices of the Survey and Excavation Projects in Egypt (SEPE), 
directed by Gregory Mumford of the University of Alabama at Birmingham. The sub- 
sequent Middle Egypt survey, shown in Figure 6.16, grew from a desire to examine 
regional settlement patterns in relationship to the well-known late Dynasty 18 site of 
Tell el-Amarna, where excavation has been undertaken for over a century by a succession 
of British and German missions, and most recently under the direction of Barry Kemp 
(Cambridge University). 

Defining the limits of the overall survey area was problematic for the East Delta, while 
historical and geographical factors made it much easier to choose survey boundaries 
in Middle Egypt. A 50 x 60 km area was selected somewhat arbitrarily around Tell 
Tebilla, while a 1 5 x 30 km area was segregated within the area enclosed by Akhenaton's 
boundary stelae (defining the agricultural lands belonging to Akhetaten, or modern Tell 
el-Amarna) (Figure 6.17). Due to the broad survey area and initial limited funding, 
the author decided to test the capabilities of coarser resolution ASTER, Corona, SPOT, 
and Landsat 7 imagery in the detection of previously unknown ancient features. Some 
archaeologists may argue that Landsat and SPOT imagery can be too coarse (i.e. 30 m 
pixel resolution) to detect archaeological features in floodplain areas (Wilkinson 2002), 
but they are right only when smaller features, such as cropmarks, need to be found. 
A wide range of techniques could be tested on known sites in the Delta (Butzer 1975), 
where many surveys and excavations had already taken place, and confirmed via Corona 
imagery. At the time of this Delta research, Google Earth™ only offered SPOT 20 m 
imagery for the Delta, where more surveys have been conducted. Middle Egypt was 
quite different, as the last major survey to cover the West Bank opposite Tell el-Amarna 
took under the Napoleonic Survey of Egypt in 1798-1802. 

In the Delta, the most appropriate satellite imagery appeared to be Landsat and 
SPOT imagery dating to the wettest months (the winter), as moisture content seemed 
likeliest to aid in isolating archaeological sites. Many previous surveys had taken place 
in the East Delta (Bietak 1975; Van den Brink 1987; Chlodnicki et al. 1992, Butzer 



Figure 6.16 Middle Egypt, showing high tell in the middle of a town during 2004 Middle Egypt 
Survey Project, photograph by Sarah Parcak. 

2002), while the work of the Egyptian Exploration Society's Delta survey also proved 
invaluable for checking historical excavation and survey data. In the last two decades 
Egypt's Supreme Council for Antiquities (SCA) has placed an increasingly high priority 
on Delta archaeology through the Egyptian Antiquities Information Service (EAIS) 
recording teams, halting most new work south of Cairo and encouraging more projects 
in the Delta (Cashman 2001; El-Ghandour 2003). One hundred and nineteen known 
surface sites have been recorded in the East Delta survey area, being known through 
excavations and surveys, but also provided a base for testing varying remote sensing 
methods (Parcak 2003). Other earlier satellite studies in the Delta had not been as 
successful in differentiating between modern settlements and the ancient sites they 
covered partially or completely (Brewer et al. 1996). 

Likewise, this author's initial tests, using band combinations, band ratioing, and 
supervised and unsupervised classifications, did not work in detecting a high percentage 
of the known archaeological sites in the East Delta. However, the problem proved to 
be the spectral signatures of the known sites: They could not be readily distinguished 
from the spectral signatures of the towns and buildings on top of them. This problem 
was solved when a PCA was processed for a 4-3-2 RGB Landsat 7 image using six prin- 
cipal components. This technique enabled more subtle landscape variations to appear. 
It succeeded in detecting 90 percent of the already known archaeological sites, and 
detected 44 previously unknown sites with the same spectral signatures. The validity 






ll'cll cl-Amarnj 



Jl hum Q 


JScale 1 



^^■1^^ Ki 

Figure 6.17 Middle Egypt, overall Landsat image (scale 1: 100000 m) of 2004 survey area, image 
courtesy of NASA. 

of these "new" sites was also confirmed independently with Corona imagery prior to 
fieldwork in the summer of 2003, which visited 62 known, poorly known, or previously 
unknown sites. Overall, the remote sensing techniques and subsequent survey attained 
a 93 percent success rate for detecting previously unknown archaeological sites. The sur- 
vey located and recorded surface material culture for comparison to the already known 
survey and excavation materials (Parcak 2004a). 

The Middle Egypt survey project applied the same types of satellite datasets, and, 
with additional funding, also used Quickbird data. Unlike the Delta survey, the exact 
location of other ancient sites was not known in the floodplain across the Nile from 
Tell el-Amarna (Lepsius 1859). Hence, remote sensing techniques were tested on the 
nearest known site at el-Ashmunein (Spencer 1982, 1989, 1993), shown in Figure 6.18, 
a large, multi-period tell located 10 km to the north of the intended survey area. Running 
an unsupervised classification on a 4-3-2 RBG Landsat image clarified the spectral 
signature of the known archaeological part of el-Ashmunein, seen in Figures 6.19a and 
6.19b, and detecting a surprising 70 additional "ancient site" signatures (27 of which 
had already been recognized through brief mentions in past publications, but had never 
been surveyed in detail; see Kemp 2005). The remaining 43 previously unknown site 
surfaces that appeared were confirmed in both the Corona and Quickbird data. Ground 
truthing took place in Spring 2004, visiting 69 of the 70 sites found in the remote sensing 
study (one site could not be visited due to lack of a bridge across a canal), which located 




Figure 6.18 Ashmunein, Middle Egypt, photograph by Sarah Parcak. 

Figure 6.19 Ashmunein, Middle Egypt: (a) classified Landsat 4-3-2 image (scale 1:875 m), the 
white pixels represent archaeological material, while the darker pixels represent 
mixed archaeological debris; and (b) the modern town of Ashmunein, compare it 
with the 2008 Quickbird image (scale 1:806 m) of Ashmunein, images courtesy of 
NASA and Google. 



ancient material culture at 98 percent of the previously unknown sites (Parcak 2005, 

The higher pixel value associated with the "ancient site" signatures sites detected in 
the remote sensing analysis seems connected to the relative overall moisture content of 
the soil. For instance, tell sites in Egypt, and the Middle East, in general, are formed by 
a complex series of processes (Schiffer 1987; Steadman 2000), and are normally covered 
with layers of eroded clay and silty soil, beneath which lies higher concentrations of 
mudbrick, the main building material for the ancient Egyptians. This soil is full of 
organic material, which alters its ability to retain moisture. Compounds called hummous 
colloids are created from chemicals. These compounds change the vegetation's vigor, and 
then transform chemically the moisture on the mound (Davidson and Shackley 1985; 
Limp 1992b: 34; Rapp and Hill 1998; French 2002). It appears to be this chemically 
altered moisture that is being detected by the PCA in the East Delta survey region 
(after the spectral redundancy has been removed from the image) and by classification 
techniques in the Middle Egypt survey area. The success of these two different techniques 
shows how recognizing and implementing different methods of detection can work in 
similar environments (i.e. floodplains) along the same river system, relating no doubt to 
changing weather patterns, atmospheric conditions, and longer term anthropomorphic 

Hence, the remote sensing project work in Egypt has answered questions relating to 
human— environment interaction throughout Egyptian history, yet much work remains 
to be done through future survey seasons. The overall remote sensing and ground sur- 
vey results provided much data on long-term settlement changes in the East Delta, 
some of which appear far more connected to environmental shifts than previously 
thought (Parcak 2004b). Detailed ceramic analysis in Middle Egypt revealed mostly 
late Roman occupation on the West Bank, and indicated intensive trade between this 
region and Aswan, Libya, Alexandria, and other parts of the Mediterranean. Ongoing 
coring work in Middle Egypt is helping to reconstruct past shifts in the Nile's courses 
and related settlement placement and changes. Future research will be conducted via 
the RESCUE project (for Remote Sensing and Coring of Uncharted Egyptian Sites), 
which aspires to detect all of the Delta's previously unknown surface sites, beginning 
in summer 2009- This project will conduct detailed coring, mapping, and survey, seen 
in Figure 6.20, ideally before the sites are destroyed. If the Middle Egypt and Delta 
survey results are any indication, then there are thousands of ancient surface settlement 
sites awaiting discovery in Egypt's floodplain regions, viewed in Figures 6.21a and 
6.21b. Regarding the unknown subterranean sites that lie beneath the current detection 
range of satellite imagery, future detection must await the release and development of 
better technology. 


The studies presented here have shown how various archaeologists have approached 
satellite archaeology research designs in different landscapes. Each study has had its own 
challenges to overcome, whether it is the landscape type, issues with funding, ground 
survey difficulties, or general data synthesis. These and other projects will naturally 
have individual needs according to regional landscape types, project funding, team 
makeup, overall team expertise, and the overall project goals. By discussing both the 



Scale 1:12000 

Figure 6.20 Middle Egypt Survey 2005, the site of Kom el-Ahmar, mapped using a differential 
GPS, photograph courtesy of Sarah Parcak. 

6.21 Tell Sheikh Masara: (a) Quickbird image; and (b) photograph, note how the sheikh's 
tomb looks from space, images courtesy of Google Earth™ Pro and Sarah Parcak. 



positive and negative aspects of each project's approaches, it is hoped that archaeologists 
working in similar geographic areas or landscape types will be able to model their 
projects accordingly. Satellite archaeology is ever changing and evolving, thus, as new 
satellite imagery appears with improved spatial and spectral resolutions, project designs 
will change as well. Regarding the subsequent chapter, how projects can approach the 
collection of archaeological data on the ground forms the next logical step in research 




Without detailed ground surveys, information collected from remotely sensed satellite 
images loses much of its potential meaning. Studies can be conducted with reference 
to the application of specific techniques with great success, only if the archaeological 
situation is known in detail. For example, if 90 percent of the settlement sites from a 
particular region are known to date to the Roman period, and satellite remote sensing 
specialists locate 100 additional possible archaeological sites, it is likely that the major- 
ity of the new sites would also date to the Roman period. One would not normally, 
question such a supposition in an archaeological publication. When publishing reports, 
archaeologists risk conjecture if their studies have not implemented ground-truthing or 
previous survey work risk due to the error rates typical in all remote sensing analysis. 
It is hoped that, when attempting different applied remote sensing analytical meth- 
ods, some will work, while it is anticipated that others will not. Techniques of analysis 
that do not work are still very useful to remote sensing archaeologists, as it is through 
understanding why particular techniques do not work that one can identify techniques 
for future work that could help to locate additional archaeological features of interest. 
Understanding this from a ground-based perspective is invaluable. 

This chapter outlines the key factors for archaeological ground surveying (or "ground- 
truthing") of data collected from remotely sensed images. To what extent can satellite 
remote sensing surveys be considered as different to more typical ground based archae- 
ological surveys (Aufrecht 1994; Given et al. 1999; Orton 2000; Blakely and Horton 
2001; Kardulias 2002; Piro and Capanna 2006; Campana 2007)? They may outwardly 
appear to be virtually identical: During survey work, a team will often walk set transects 
(i.e. linear paths) across individual sites (Figure 7.1), or follow geological or natural fea- 
tures, pending landscapes differences, looking for, and often recording and collecting 
material culture. Ground surveys for satellite imagery, however, differ greatly from typ- 
ical archaeological surveys. In these circumstances archaeologists will have pinpointed 
in advance specific areas for archaeological reconnaissance. Rather than conducting a 
typical survey using arbitrary 100, 200, 300, or more meter transects across a landscape 
with teams of people (Knapp 2000), a ground-truthing team will have pre-selected 
"archaeological signatures" for verification, thus increasing survey efficiency. How the 
archaeological teams want to conduct the survey on the ground will vary depending on 
numerous factors (Alcock and Cherry 2004), which will be discussed here in detail. 



7. 1 Delta survey, Egypt, showing geoarchaeologist Larry Pavlish doing a pacing survey, 
photograph by Sarah Parcak. 

The very purpose of archaeological remote sensing work affects how and why archae- 
ologists record specific features on the ground. Different types of archaeological survey 
must also be evaluated, compared to and contrasted with satellite remote sensing 
ground- truthing. Any subsequent survey work will depend on the overall goal of the 
archaeological exploration: Remote sensing work might aim to locate new archaeological 
sites, establish cultural resource management, monitor archaeological sites under threat, 
assess general landscape risks (e.g. urban sprawl), or locate significant geomorphological 
features. Each type of remote sensing ground survey, like all surveys in archaeology, 
would require a different type of approach. 

In contrast to traditional ground survey, the remote sensing specialist must record 
everything on the ground (i.e. all vegetation, soils, and water sources) for later comparison 
with the satellite imagery to determine why a particular area yielded an "archaeological" 
signature. One should not only be concerned with locating archaeological sites, but also 
underlying archaeological site markers, which may vary widely depending on landscape 
geomorphology. These will include types of vegetation, water, soil, and local geological 
features. How any past land usage changed will also be of great interest, as key changes 
(especially in the past 30 years, encompassing multispectral remote sensing) will affect 
pixel values in land use land cover change analyses. Landscape characterization from 
space is entirely different: One may think the satellite imagery shows one thing (such as 
an open field), but finds it to be something else on the ground (such as low scrub forest). 
This is more often the case in areas not previously surveyed. 



Satellite remote sensing survey requires a full landscape analysis. Archaeologists must 
consider past, present, and possible future land-use practices at the ground-truthing 
stage. Evaluating the capabilities of certain satellite types in terms of what they can 
and cannot see on the ground is a key component of remote sensing in archaeology. 
Misinterpretation of satellite imagery is a more frequent remote sensing issue but can 
be corrected with later analyses based on ground results. In some cases, premature 
overexcitement at having found a "long-lost" feature or site without ground verification 
can lead to poor science, hype, and misinformation, detracting from the field. This is 
where a full survey becomes important: If one takes into account all the past and present 
factors affecting landscapes, information gained from remote sensing analysis (including 
aerial photography and different satellite imagery types) can be tested on the ground, 
and applied to a broader area. Good science is the ultimate aim of remote sensing, in 
order to reconstruct past historical and environmental events as well as landscape use 

New developments in satellite technology require modified approaches to surveying 
and excavation. What previously could not be seen or detected on the ground due to 
spatial or spectral resolution might now be seen from space (Figure 7.2). Increasing 
refinement of spatial and spectral resolution in satellite imagery allows archaeologists to 
pinpoint and visit previously unknown features on the ground. In turn, such improve- 
ments will enable survey to be further refined. The need for survey work in rapidly 
changing landscapes affects how archaeologists use and interpret satellite imagery, and 
how they adapt known remote sensing techniques to their purposes. Currently, few 

Figure 7.2 Middle Egypt, showing reverse cropmarks (the dense mudbrick architecture is 
preventing the growth of the cotton), 2008 image courtesy of Google Earth™ Pro. 



archaeologists have the technical skills to program their own algorithms and remote 
sensing programs, nor, at this stage is it generally needed, but like other disciplines 
(geology, atmospheric science and health) that have advanced to this stage, archaeology 
will, in time, adapt this and subsequent technological advances. 

One of the main issues for discussion is the overall role of remote sensing in 
archaeology: Although remote sensing is not yet a standard tool for archaeological 
surveys, it will become so in 5—10 years. With many archaeology and anthropology 
graduate programs now offering, if not requiring, some form of GIS and remote sensing 
in their curriculum, students have adapted their programs accordingly to such recent 
needs. The growth in researchers and students utilizing remote sensing has caused a 
new way of thinking in archaeology. While the specific remote sensing methodology 
faces continuous development, surveys will only continue to improve based on new ways 
of interpreting satellite imagery. Remote sensing is used far less to detect singular fea- 
tures on single sites for subsequent excavation, but this, too, is quickly changing (Parcak 
2007a). At the very least, visually interpreted satellite imagery can aid mapping, artifact 
plotting, and identifying correlations between archaeological and geological features for 
surveys, which requires limited technical skill and training other than the use of a 
graphics design program. 

As the field of archaeological remote sensing is still relatively young, it is prone to 
trial and error, especially when applying methodologies in the field. This is where the 
recording part of the ground survey becomes so critical. Many parts of the world are 
underserved by existing remote sensing work; and even less so with the applied remote 
sensing work required for archaeology. Hence, any attempts at analytical remote sensing 
methods may be devoid of any local reference points. 

Worldwide, in general, each region and sub-region will pose problems for remote 
sensing and archaeological survey work due to the nature of satellite imagery analysis. 
Here it is essential to know what satellites can and cannot detect (e.g. ancient canals 
or buried walls). Paying close attention to the additional factors affecting recording can 
help with initial survey work. Developing specific survey forms for each region is crucial 
(Figure 7.3), and will be discussed in detail in this chapter. Each team can adapt such 
survey forms to their own needs, but the suggested format provides a good starting point 
already tested during a number of remote sensing ground survey seasons in floodplain 
environments in Egypt. 

Cost— benefit analysis should be central to any discussion of how remote sensing can 
aid regular survey. How much can a single survey gain from using remote sensing as 
opposed to a regular foot survey? This author's own research in Middle Egypt furnished 
one example. It is important to demonstrate the specific advantages and disadvantages 
of incorporating satellite imagery over a sole reliance on traditional survey techniques. 
Thirty-five years ago, before the adoption of satellite remote sensing in general, one 
would need to compile lists of known archaeological sites in the survey areas and place 
them onto modern maps. In the case of the current study, the parameters for choosing 
the survey area included selecting Middle Egypt because it remained mostly poorly 
studied from the perspective of analyzing settlement patterns through time (Kemp 
2005). Instead of evaluating satellite remote sensing techniques, one can test more 
general ground surveying techniques: Pacing set distances such as 100 m transects, or 
randomly choosing areas (quadrants) for more intensive searching. Even using aerial 
photographs, many surface sites could not be located, being buried beneath modern 



Figure 73 Sarah Parcak using a differential GPS during the Middle Egypt Survey Project 2005 
season, photograph by Sarah Parcak (using a tripod). 

towns and villages. For the sake of direct comparison, in this hypothetical study only 
one person would be responsible for locating the sites, accompanied by an Egyptian 
inspector from the Supreme Council for Antiquities (SCA), a driver and a policeman 
(Figure 7.4). 

In initiating foot surveys without the benefit of pre-selected target sites, one imme- 
diately runs into various problems. For example, approaching a surface survey of Middle 
Egypt, one is faced with a former floodplain region characterized by a vast area of rice, 
cotton, and sugarcane cultivation (Figure 7.5). The first question is where should one 
begin? In regards to security issues, any current archaeological survey team would be 
permitted to visit only six places in one day and must tell the police exactly where they 
are going 24 hours in advance. The study area in Middle Egypt spans 15 x 30 km, 
much of which is inaccessible to field walking since large tracts of cultivation often lie 
beneath water. Furthermore, one cannot walk in fields without obtaining each owner's 
permission, while parasites in irrigation water offer serious health hazards. Navigation 
problems arise through the numerous canals and channels obstructing potential walking 
routes. This leaves main and secondary road connecting towns and villages for the actual 
survey route. 

Other problems arise. Where would a traditional survey team look for sites in modern 
towns and what would they hope to find? A number of modern settlements in Middle 
Egypt are large enough to represent small cities. Unless an exact area within a town is 



Figure 7. 4 Middle Egypt Survey Project team (driver and policeman), 2004, photograph by Sarah 
Parcak (using a tripod). 

Figure 7.5 Fields in Middle Egypt, photograph by Sarah Parcak. 



known to contain archaeological remains, one could spend days, if not weeks or months, 
wandering through it and finding nothing. Perhaps questioning the local inhabitants 
of towns or villages could reveal the location of archaeological features, or indeed entire 
ancient towns. Local people may often deny the presence of antiquities, fearing confis- 
cation of land or other official interference, or may simply remain ignorant of ancient 
remains other than for well-known sites. Even if an elderly man or woman remembers 
a free-standing archaeological site, they might not remember its exact location. Such 
detailed questioning and verification require additional time. If the archaeological site 
proved to be in the vicinity of modern agricultural fields, it could take many hours or 
days of searching along dirt roads or footpaths, often traveling on foot since few tracks 
in fields are wide enough to accommodate cars and trucks. Not knowing the exact 
coordinates of an archaeological site introduces another problem: By being even several 
hundred meters off track, one can easily enter an unproductive field system and miss the 
most knowledgeable farmers who are aware of the existence of ancient remains. 

An additional consideration represents the time needed for traditional surface survey 
work. Aside from the area covered by the fields, over 160 towns and villages lie in the 
Middle Egypt survey area (Figure 7.6). If one could visit six places per day for one hour 
each it would take 27 days to see every town and village, including traveling time. 
Unfortunately, the range of town sizes would require from one hour to at least one 
week to search every street thoroughly for archaeological remains (40 hours of work, 
entailing walking up and down streets and speaking to local people). Hence, it would 
take a minimum of five full-time months to visit every town in the survey region. A 
survey of this nature would have already become quite costly, especially considering that 

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7.6 Urban surveying, Egypt, note the steep angle of the parked car, photograph by Sarah 



less than one percent of the survey area is classified as "urban", leaving over 300 times 
the area and time required to complete a full foot survey with 100 percent coverage 
(See Table 7.1). Assuming one could visit field systems, problems of accessibility and 
transportation might obscure any potential results. On foot, one could reasonably, albeit 
less extensively, search a 2 x 2 km quadrant in an eight-hour day, assuming that the 
fields were connected by good footpaths. It would thus take a minimum of 112 work- 
days to field walk the entire survey region (measuring 450 km ), which is 4.5 months. 
Thus, to survey both the urban and field areas on foot using traditional survey methods 
would take a minimum of 9-5 months. Adding in typical survey delays and a break 
on Fridays, problems with transportation and bureaucracy, the survey would likely take 
about one year. In contrast, the Middle Egypt Survey Project had obtained pre-selected 
target areas from satellite images analyzed and surveyed from the same region in only 
two weeks (14 days), which represents a savings in time of 2600 percent. This does 
not include the time spent prior to the survey season analyzing the satellite imagery, 
preparing for the survey, or processing the collected archaeological material culture, but 
all surveys must spend time on such activities (minus the imagery analysis). 

Cost is also a major consideration for large-scale surveys, but the overall purpose of 
the actual survey cannot be forgotten. Typical surveys must pay for a driver, vehicle, 
accommodation, food, workmen (if needed), salaries for officials or specialists, supplies 
and equipment. Being able to survey 26 times more efficiently allows for more time 
in the field with additionally much reduced costs, as well as decreased wear on techni- 
cal equipment. This is, however, representative of regional surveys searching for large 
sites or features, whose goals remain very different than the goals of most field walk- 
ing teams. Remote sensing survey is different in this respect: Satellites cannot, at this 
moment in time, detect small sherd or lithic scatters in fields. Tiny objects detected 
with the naked eye represent major differences between satellite imagery analysis and 
the power of the human eye. Small scatters of objects found across landscapes add up 
to a much bigger landscape picture, addressing more subtle landscape usages. Ideally, 
both types of surveys can be carried out in an area, but as shown by the Middle Egypt 
example, "typical" ground surveys are not always possible, or easy, depending upon the 
local terrain. 

Once teams have located larger sites or features through a remote sensing survey, it is 
possible for future teams to return to those areas to conduct more typical field walking 
in the areas across and surrounding these places. Future intensive ground survey may 
be advisable, especially if additional features appear on the ground that did not appear 
during the satellite imagery analysis. Sherd(s) and lithic scatters, or remnants from earlier 
sites and features, will likely appear in the areas surrounding the relocated archaeological 
sites (Figure 7.7). This represents an ideal marriage of field walking and remote sensing 
survey. This is most useful in larger landscapes that are difficult to traverse, or where 
typical ground surveys are not possible to conduct, such as desert regions, where sites 
often cluster around surviving (and ancient) water sources possibly detectable from space. 
Additional site features not detected by satellite imagery analysis will take more time 
for the ground survey teams, and must be carefully plotted, to address why they did not 
appear during the original analysis. These unexpected features can be just as valuable as 
located features, as they force the team to consider running alternative analytical remote 
sensing techniques to isolate them, which, if successful, may allow an even greater 
number of features to be detected in future survey seasons. 



Figure 7. 7 Sherd scatters in a field, Middle Egypt Survey Project 2004, photograph by Sarah 

During ground surveys, teams must consider the anthropological side of survey work 
just as seriously as the remote sensing itself. Local interactions can provide the most 
fruitful information exchanges, especially regarding landscape use changes, archaeolog- 
ical site destruction, and future land-use practices. Initially, it is suggested that a team 
identifies and meets the leaders of a village or group of individuals, and discusses the 
survey project with them. Honesty is the best policy, and it may be that the survey 
team can aid local farmers with detailed hydrological maps or maps showing places 
where wells can be drilled. Although this may take a considerable amount of time at the 
outset, building good relationships may streamline work permits or may enable further 
survey work in otherwise forbidden areas. With remote sensing, plans change, and you 
do not always know where you will go. Information travels quickly via local networks 
in rural locations, which is why time needs to be set aside into the survey schedule for 
consulting local people. At no point should money exchange hands, unless a specific 
service was rendered (i.e. car fixed, tools purchased, etc.). Issuing gratuities will foster 
increasing expectations and costs to survey seasons, and may encourage local people to 
pillage sites and antiquities to earn extra money, thereby setting a dangerous precedent. 
People should be thanked, but only in culturally acceptable ways. Knowing what is 
and is not culturally acceptable prior to a survey season will help. Small gifts of tea, 
fruit, or sweets are always appropriate if a local person takes significant time to show an 
archaeological site or feature. Carrying a Polaroid camera will allow an immediate gift 
of photographs, which are always much appreciated by people who often lack photos of 
their families. Building trust is far more crucial, especially in areas where local politics 
can interfere with work progress. In all of the survey work the author has conducted, 



Figure 7.8 Middle Egypt Survey Project 2004, children who helped survey, photograph by Sarah 
Parcak (using a tripod). 

no individual has ever asked for money for taking time to aid in the survey process. In 
several instances, individuals actually made it clear that they did not want or expect 
any money, and that they appreciated interest in their cultural heritage. Returning in 
subsequent years to conduct coring or additional survey work, this author found that 
entire villages would come out to greet her. In several cases, local people gave intact 
ancient pots to the author (who turned them over immediately to the Egyptian SCA), 
saying they had been found during the construction of their homes. 

Elderly adults represent only one group of people that should be questioned during 
remote sensing ground-survey. Children are often the best resource for locating smaller 
features that may not have been noted on the satellite imagery (Figure 7.8). If accom- 
panied by government officials, children tend to be shooed away. Small bribes of sweets 
(again, remembering cultural appropriateness) work wonders during foot surveys, espe- 
cially in larger towns that may cover a site or feature partially. Asking children directly 
for help has assisted this author and many of her colleagues during surveys, as children 
tend to know each nook and cranny of a town. Posing the right questions to local indi- 
viduals can be difficult, as each person will have their own version of local history. This 
is to be expected: Each place will have multiple "histories" from multiple perspectives. 
How each individual signifies change to a landscape is also important to note, and will 
change depending on their position. 

Additional questioning of locals will vary depending on what the team wishes to 
find. People are open about how landscapes and crop pattern have changed over time, 
and may be willing to show things that have appeared in fields or close to their homes. 
Farmers are also likely to know about cropmark patterns, and may be able to comment 



Figure 7.9 Tell ed-Da'ba, East Delta (scale 1:99 m), note extensive cropmarks in the field, 2008 
image courtesy of Google Earth™ Pro. 

on how well they appear in certain types of crops. In Egypt, cotton and rice paddies 
show buried mudbrick walls clearly, whereas fields of corn show walls less clearly (from 
the ground). Different crops will grow higher or lower pending the buried archaeo- 
logical features, and studying cropmark patterning in other parts of the world (seen in 
Figure 7.9) will aid in this process if there are no previous cropmark records in a partic- 
ular survey area. The wet time of year may show brief snapshots of buried walls in desert 
environments, especially as plants grow quickly over a short period of time. In Jordan, 
for example, Nabatean fields have appeared in the deserts during the brief wet season 
(Glueck 1965). 

Visiting the local or regional land-use or agricultural office may assist with learning 
about general landscape changes. They may have records regarding field boundaries 
changes, site names, crop usage, population density, and urbanization changes over the 
past 200 years, or more, which will change across regions in the same country: Each 
area has its own distinct, individual history depending on resource allocation, growth, 
expansion, and collapse over time. This stretches beyond the roughly 50-year time 
span of local living memory, which may or may not be necessary depending on the 
needs of the survey team. Geology or water-management departments in universities 
represent additional points of contact for survey teams. These offices often have access to 
high-quality maps and aerial photographs. Questions should be asked of the antiquities 
authorities regarding how much can be sampled from surveying sites, stored locally, or 
transported abroad. This will affect general survey collection strategies, and shows the 
importance of partnering with local institutions who might be able to store or study 



items in more detail, and may be able to save money by conducting scientific analyses 
in the host country. 

For any survey, archaeologists must question the reliability of information collected 
from local individuals regarding recent landscape change as well as archaeological site 
location. Even when conducting fieldwork with precise GPS coordinates for archaeolog- 
ical sites, it can be difficult orienting oneself on the ground. Roads may have changed or 
have gone out of use. In-field directions to archaeological sites can be highly problem- 
atic, and this is not surprising. Imagine if a stranger or group of strangers, accompanied 
by officials and police, were to stop and knock on your door, asking for directions to 
a specific place known only to your community. Perhaps this place is sacred (e.g. a 
shrine or cemetery covering an archaeological mound), or perhaps it is a place you, your 
family, friends, or neighbors rely on for income for food and clothing. Why would you 
feel obligated to direct the group of foreigners to the exact location? There are always 
multiple layers and internal or external pressures and factors that we do not or cannot 
realize that may affect the veracity of information received. Aside from speaking the local 
language, following local customs, or hiring local workers, which all aid survey work, 
this is a reality that archaeologists must respect and understand from the perspective of 
local groups. 

In the majority of cases, however, local people remain the most valuable resource for 
archaeological remote sensing surveys. One is most likely to encounter farmers in rural 
areas, and what better people to make inquiries regarding landscape changes, land-use 
practices and crop usage at different times of year? Farmers know their own land and its 
immediate history better than any local or regional government organization. Asking 
what types of fertilizers were used, or when fields were irrigated, will also aid in the 
post-processing of satellite imagery data. It may be that such factors influence whether 
certain types of archaeological sites appear in satellite imagery. Farmers have taken the 
author to numerous archaeological sites not otherwise discovered in her initial remote 
sensing analysis, which, in turn, assisted with locating sites with further imagery analysis 
(Figure 7.10). Farmers also know a great deal about the archaeological material on their 
land. In some cases, they can discuss to what time period a particular site might date 
(i.e. Roman versus earlier), and why they think it dates to that period of time, with great 

Ethical issues abound when conducting ground surveys, especially regarding the 
illegal antiquities trade. In addition, maintaining colonial or ethnocentric views can 
lead to mistrust and even obstacles regarding what locals may or may not have recov- 
ered on their land or nearby. If locals claim to have discovered "treasure" (e.g. 500 jade 
masks from a particular site in Central America), it is likely that significant leg-pulling 
is occurring. However, if people claim they found more mundane items, such as con- 
centrations of broken pottery (Figure 7.11) and bones in an adjacent field, this is more 
readily believable. Conversely, if someone were to inform an archaeological team about 
locally found objects (e.g. statuary, complete vessels, etc.), the local inhabitants could 
easily become suspicious when the foreigners ask to see such objects stored (illegally) 
in their homes. Gaining access to such objects may take years of building trust, but 
what is the survey team legally bound to do from an ethical perspective should this 
happen? Theoretically, archaeologists should follow a strict ethical code regarding the 
looting and illegal antiquities trade. Yet stopping this trade cannot happen overnight. 
Convincing local people about the potential employment opportunities from future 



Figure 7.10 Farmer, Middle Egypt, photograph by Sarah Parcak. 

Figure 7.11 Pottery, Middle Egypt, photograph by Sarah Parcak. 



excavations being conducted on a previously undiscovered archaeological site may not 
outweigh the money they might receive from plundering and selling items to dealers. 
Discovering a previously unknown archaeological site through remote sensing, which 
then proves to be looted, may also have undesirable consequences for the survey team. 

This should be discussed with local authorities (which may not be possible if they 
are involved with the looting) or assessed by speaking to trusted local sources. How to 
proceed in such circumstances will require discussion and rigorous debate, especially as 
the field of archaeological remote sensing develops. 

Ethnoarchaeology also has applications to remote sensing surveys. Where land use 
practices have not changed much over the millennia, interviewing people about their 
farming practices and observing these techniques might contribute towards understand- 
ing past land-use strategies. One pertinent example comes from the site of Angkor Wat 
(as discussed in Ch. 6), where radar survey has revealed the site to be significantly larger 
than previously thought (Evans et al. 2007). Water management issues have not dimin- 
ished in significance over time, as past water distribution systems observed from space 
and confirmed on the ground appeared to follow more "modern" strategies. Answering 
these and related questions can be just as important as locating archaeological sites. 
Studying modern land-use practices can help understanding of how and why past sites 
might appear during the satellite imagery analysis, which can, in turn, help to locate 
additional sites and features. 

Discussing landscapes with local people and sharing survey data with them requires 
a degree of caution. Military regulations differ between countries and while Google 
Earth™ is publicly available for sensitive areas it is not always wise to carry around wide- 
format printouts of satellite imagery. This will vary across the world, but it is always 
wise to err on the side of caution during surveying. For example, during a 2002 survey 
of South Sinai, this author, accompanied by her survey team, a driver, and an Inspector 
from the SCA (required by the Egyptian government for all foreign expeditions), nearly 
got arrested for entering an unmarked security zone. The inspector, who had worked 
in South Sinai for 15 years, knew the survey area fairly well: He avoided variously 
marked military areas and compounds, following what appeared to be a non-military 
road into a large wadi, where we commenced with a survey atop a ridge overlooking 
the wadi. During a search for ancient material culture, we noted a group of uniformed, 
armed men running towards the car, and decided to return to the car. Our inspector was 
having a heated discussion with an officer, and he instructed us to "get into the car." 
We kept our maps, cameras, and GPS units concealed so as not to worsen the situation. 
The officer looked briefly into the car, noticed nothing, and let us go. Our inspector 
subsequently explained that we had entered an unmarked military area, of which he 
had been unaware, and that he had convinced the officers that we were nothing more 
than simple tourists who did not know any better. We narrowly avoided being arrested, 
but if they had found our survey equipment, we would certainly have encountered 
more serious ramifications, despite our antiquities permission to survey in this region. 
This unforeseeable situation was fortunately remedied by a highly competent and quick 
thinking Egyptian colleague. We would not have surveyed there had we known it was a 
military area. 

Coordinating with local authorities sensitively is crucial to avoid this and other 
situations, given all the irregularities of ground- truthing. Advance planning for all 
contingencies can minimize any potential problems. A survey is not worth risking the 



life, career, or health of any team member, and all team members must be advised of all 
possible ramifications of fieldwork. Dog whistles are critical, given the large numbers 
of stray dogs (with rabies) that reside near archaeological sites. In another survey region 
in Central America, Tom Severs team was held up at gunpoint (Sever 1998: 158). They, 
too, had luck on their side. Every remote senser in archaeology has a story to tell about the 
adventures and perils of fieldwork, and it would be beneficial to collect these stories into 
a compendium of "what not to do" to prevent problems in future survey efforts. Addi- 
tional publications, online forums, blogs, and courses can also pass on this information, 
but more is needed to promote good survey practices in archaeological remote sensing. 

Along with knowing the potential dangers inherent in conducting ground- truthing, 
timing is also critical for carrying out surveys, regarding both time of year and time of 
day. Surveys should, if possible, coincide with the season and time of day the satellite took 
the original imagery. Features may not be visible on the ground during other times of 
the day or year without test trenching, which may not prove feasible during surveys. Dry 
or wet conditions may prove optimal depending on the survey's global location. Either 
condition may allow for additional features to be observed by the survey team. Local 
planting cycles may allow or prevent survey work, especially in areas inundated once or 
more times per year. This would thwart the collection of material culture in fields. The 
dry season would be best for conducting surveys in southeast Asia. For example, daily 
torrential rainstorms do not permit the use of non- waterproof equipment, or may inter- 
fere with GPS signals in rainforest environments (in addition to the overlying foliage). 
Cloudy days may also cause reception problems for satellite receiving stations. Issues 
that affect normal foot surveys, such as extreme temperatures, affect ground- truthing 
surveys in a similar, but not entirely related way: Typical ground collection survey may 
work, for example, from 6 a.m.— 2 p.m. in certain regions, with post-processing occur- 
ring in the afternoons at the nearest convenient or proper facility. Satellite survey work 
is less predictable: Even though a survey team may know the general area where it will 
be working, and may have selected a set number of sites to be visited on a particular day, 
vehicular problems, washed out roads, or bureaucratic delays may all combine to make 
the work day significantly longer and less productive than expected. 

Given that satellite remote sensing will have detected a certain number of "new" 
features or sites in a given region, the goal of the survey team will be to visit as many 
places as possible. This is one way that ground- truthing of satellite imagery differs 
markedly from foot survey: One seldom knows exactly what will appear in a transect 
survey, including even the general size of a site (despite predictive modeling), whereas 
remote sensing enables one to estimate the general size of features located on the ground 
(Figure 7.12). During ground survey, given that team members will be walking in set 
transects across a landscape, one can estimate the general area to be covered over a day 
or over the course of a survey season. With ground- truthing, everything changes when 
teams begin to search for sites on the ground. Although they may know exactly where to 
go to locate the site signatures, the specific things generating such data remain unknown 
and need to be verified. Striving for efficiency in surveying must be balanced against 
a myriad of unknown variables and obstacles to be encountered. Even in areas already 
visited by ground-truthing, the one common factor in archaeological surveys across 
the globe is to expect the unexpected. For example, on a tragic note, Bedouin bandits 
murdered explorer Captain Palmer during his survey work in the Sinai Peninsula in the 
late 1800s (Wilson 1976). 



Figure 7.12 East Delta survey, cemetery originally appeared as a modern town covering an ancient 
site during imagery analysis, photograph by Sarah Parcak. 

A less dramatic, but pertinent example, occurred in October 2007, when the author 
participated in a documentary on environmental change in ancient Egypt. As part of the 
footage, the documentary team filmed the author prospecting a large archaeological site 
in the northeast Delta, examining surface potsherds, essentially showing how archae- 
ologists collect surface data to show periods of settlement. During the initial summer 
2003 survey, thousands of sherd(s) (dating to multiple time periods) covered the site 
to such an extent it proved impossible to see the silt surface in many places. However, 
under different weather conditions, only thick Roman period sherd(s) appeared, with 
2—3 inches of light silt covering most of the site. It did not seem likely that thousands 
of sherd(s) had simply disappeared. One needs to think about general micro-landscape 
changes due to seasonality: For instance, October is a time of transition in the northeast 
Delta and throughout much of Egypt. Having completed the summer and early fall 
harvests, farmers burn their fields to ready them for the next planting cycle. August 
and September are dry months, and with many fires in the fields, dust and ash can be 
thick in the air, and aid in the accumulation of a loose surface layer on higher ground 
(Figure 7.13). This is why few sherd(s) appeared on the surface of the survey site: Buried 
beneath few inches of powdery surface silt, the sherd(s) would not reappear until after the 
winter rains (which would either wash away the silt or delineate the subsurface pottery). 

It took this visit to realize just how crucial a spring to mid-summer visit is to carry out 
an effective ground survey on large multi-period settlement sites in Egypt. In contrast, 
the winter rain turns archaeological sites into mud and makes survey unpleasant if 



tit ;_ i*. — **■ * 

if (Altai 


- , F > - 

Figure 7.13 Tell Sharufa, East Delta 2003 Delta survey, note dense vegetation covering the site, 
photograph by Sarah Parcak. 

not impossible. This is a good lesson for carrying out ground-truthing in other regions: 
Seasonality can make the difference between a successful and unsuccessful season. 
Regardless of the success in locating archaeological sites or remains, remote sensers 
in archaeology cannot accept at face value the current status of traditional survey results 
for a given region. Just because archaeological survey reports may claim something does 
not exist within a given region does not mean it is not actually there. 

More critical thinking is required. Archaeological remote sensers must conceptualize 
a greater range of landscape possibilities. As an example, the author has viewed crop- 
marks surrounding the site of Tanis in Egypt's East Delta, which suggest the multiperiod 
archaeological landscape in the vicinity to be 20 times its current size. Confirmation of 
such remote sensing work naturally awaits ground-truthing. Other remote sensing has 
located environmental features in Central America that have helped archaeologists to 
understand how the Maya flourished, and ultimately, how they might have collapsed 
(Saturno et al. 2007). No result in archaeological remote sensing analysis is too insignif- 
icant or odd to investigate. A strange signature on a satellite image might represent 
a modern village, or it might represent an archaeological feature heretofore unknown 
in a particular region. This does not mean that remote sensing specialists should be 
prone to flights of fancy: Satellite imagery analysis is a scientific exercise that requires 
rigorous testing and close examination of any potential false positives. The full range of 
possibilities must be considered, as remote sensing has already revealed just how little 
we actually know about ancient landscapes and landscape usage. The results of such 



analysis affect the choices ground survey teams can make in planning sufficient survey 

Remote sensing changes traditional ground survey by allowing the team to locate 
and choose which sites to visit in advance. Thus, how does remote sensing aid in general 
pre-survey planning? If one has a good idea of the types of sites to be visited, and 
hence the types of features to be encountered, a team can bring appropriate surveying 
equipment into the field. Surveying schedules can be made, and specialists can be brought 
accordingly. Ideally, each site can be field walked, with a topographic plan generated 
rapidly by total stations or a differential GPS. Coring, if possible, should be initiated to 
understand the full date-range of the site, and ideally excavation of a few test trenches. 
All of these steps take significant time, but if carried out, may allow only one site to 
be visited each day. This is why advance planning becomes so important: How the 
team wishes to approach each site may be different, with some sites selected as "key" 
targets (perhaps displaying the largest or most readily apparent features) that require 
more intensive examinations. Other sites may be threatened by modern development, 
and need to be surveyed in greater detail before the information is lost. Collection and 
surveying strategies abound, and everything depends on the specific region where a team 
is working. There is no one right way to survey, although different archaeologists may 
debate this topic at great length. Each situation will determine the most appropriate 
specialized approach. In addition, it may not be possible to collect material culture at 
each site for analysis, due to significant restraints on time. Other situations may not 
allow detailed interviewing of local people. However, knowing where to go in advance 
of the survey provides a good starting point, while plans for future surveying seasons 
will affect the approaches a team may take in the initial season. 

Although each survey will have individual needs based on landscape types, the size 
of the recording team, and the specific archaeological circumstances encountered, it is 
an essential to adapt a basic archaeological ground-truthing form for recording survey 
data. While consistency is a key component in recording the ground-collected data, it 
is assumed that each team will adapt their forms to any local in-field needs, as well as 
refine them over subsequent surveying seasons. In the course of three survey seasons in 
Egypt, the author made numerous changes to her forms, and foresees additional changes 
in future seasons. Landscapes are consistent only in their ability to remain in flux with 
modernization and global warming, and subsequently archaeologists need to remain 
flexible in their approach to recording them. 

In the process of preparing for the ground-truthing survey, the team will need to 
determine how large an area to tackle. Part of this decision will depend on the budget, 
timing, team size, the amount of detail the team wishes to extract from the survey 
region, and the permission granted by the government in question. One of the major 
issues with archaeological satellite remote sensing is that it is relatively easy to analyze 
in comparison to the time it takes to survey, especially with larger area imagery such 
as Landsat or SPOT. The normally more costly Quickbird imagery, ordered in advance 
by the team, often limits the survey region to smaller areas. As in all remote sensing 
analyses, it may transpire that a team only wishes to investigate a small number of 
features or anomalies appearing in the imagery in question. In these cases, assessing the 
overall area is not as essential. Remote sensing allows for a larger geographic coverage 
to be investigated given that a team can visit specific locations rather than conducting a 
random intensive search. However, attempting to cover too large an area can affect survey 



quality. Biting off more than one can chew in an initial survey season can be a beneficial 
for gauging future survey logistics. Only by an initial survey season can a team learn and 
plan from how much can be done in a set amount of time with limited resources, and a 
certain number of team members, and potential unknown variables, which include host 
government restrictions, weather and unpredictable transportation. Each host nation 
and region will differ, and specific survey timing depends on the individual goals of 
each survey team. Once a team learns its specific limitations and in-field problems, 
future surveys can be conducted with greater efficiency. 

Large areas of negative space may appear in the remote sensing analysis, and it is 
up to each team to decide how much time should be spent testing areas where no 
apparent archaeological surface features are indicated. Further time should be devoted 
to answering why a particular area is devoid of sites. In addition, researchers should 
test the validity of the remote sensing analysis, but it is also expected that remote 
sensing will not identify 100 percent of the surface features during the first season, 
or indeed, in any season. In some cases, what may appear to be definite archaeological 
features may reflect something else entirely, shown in Figure 7.14. Farmers have used 
soils from archaeological sites as fertilizers for a long period of the time in the Middle 
East, hence "false" signatures may not reveal distinct archaeological sites, but instead 
may reflect how land-use practices have changed. Until survey has begun, one will 
not know the total limitations of initial remote sensing analysis, and there will always 
be limitations. 

Figure 7.14 East Delta Survey, what appeared to be vegetation on the satellite image turned out 
to be crops covering an archaeological site, photograph by Sarah Parcak. 



Concerning the establishment of a survey area's size, one example is provided in 
Egypt's East Delta. Testing the results from a wide number of site types over a large 
region (50 x 60 km) allowed the refinement of a similar remote sensing surface survey 
methodology in the subsequent Middle Egypt Survey Project. The Delta survey expe- 
rience enabled improvements in the overall Middle Egypt Survey Project approach and 
results, which was necessary given tighter police restrictions. The smaller 15 x 30 km 
area in Middle Egypt also permitted a more intensive recording of each site. Instead 
of looking for sites that previous surveys had identified, the Middle Egypt Survey 
Project located and focused mainly on previously unknown archaeological sites. Given 
the problems encountered with site location in the Delta survey (e.g. issues with map 
accuracy, blocked roads, and a much larger area), the author restructured the Middle 
Egypt survey appropriately, choosing the order of sites to be visited in advance. This 
allowed for greater coordination with the required police escorts, which needed to liaise 
in advance and coordinate with other police escorts in an adjacent province partially 
covered by the author's survey. Instead of simply prohibiting the author from conduct- 
ing her survey in different jurisdictions, the police kindly pre-approved the list each 
evening, and even supplied an exceptionally bright officer to assist the author with her 
GPS readings. 

Once a team decides on the overall survey area, it is useful to generate detailed maps 
by draping contours over satellite imagery on ArcView®/ArcGIS®, which can then be 
printed in large format. Information from local maps, if available, can also be digitized 
and added to the georeferenced satellite maps. Each located site or feature in question 
will thus already be placed on the map, and can even be labeled in the potential order to 
be visited. The local antiquities office, or government agency in charge of archaeological 
permit granting, should also receive a copy. Hence, team members will then have access 
to an up-to-date reference tool, which is otherwise lacking or unavailable commercially 
in many parts of the world and which is indispensable for quality ground-truthing. 

While ground-truthing, pending access to electricity, or a portable generator, it may 
be easier to record directly into a handheld palm desk accessory (PDA). Hard copy 
recording may be preferable, and is suggested in combination with PDA recordings 
given the likelihood of electronic failure in rural areas, or issues with excess heat and 
moisture in the field. One of the advantages of a handheld PDA recording is being able to 
import data to and from a database in ArcGIS® while examining the satellite data. One 
can also mark off specific locations previously visited during the survey. Highly durable 
computers for tough field conditions are now publicly available, but their higher cost 
may be prohibitive for modest project budgets. Printing facilities would also provide the 
survey data in a hardcopy format, enabling multiple bound copies for ease of comparison 
during the course of the season. 

Recording sheet overview 

Regarding the format of the recording sheets and booklet, each team may have personal 
preferences, but two pages (double-sided) per site usually allows for sufficient detailed 
recording. The author utilized this format, with the first page being graph paper (per- 
mitting a rough surface survey sketch), with, if possible, the outline of the site or feature 
in question pre-drawn to-scale (Figure 7.15). If one can print out the appropriate part 
of the satellite image containing each site or feature, with an accompanying modern map 




Site Number/Name: Singire 

Eastings/Northings of Site on Landsat: 

.. \ <^V-A Yl*^ 

Near Villages (Names- ancient/modern): in eastern part of town UesjW L *f 

Found on Mans: 19211s Map, Tanda £*/**t*jM* 

Found on what Corona series: Corona 6, 1111, 1972 

Site size (Landsat): \ 

Site size (Corona): A ^- ^tAf-^ XhM 

Actual site dimensions on the ground: ■* 5^* 

Genera] shape of tell: 

% covered by modern structures: 

Who owns Land : 

Description of lands surrounding the site, including irrigation systems 


Is it possible to see crop marks in the fields? Describe? 

Architectural features observable on the surface 
Describe the highest point on the mound 

What does Local knowledge say about the site (when destroyed, what found, anything in fields, 
how large was It 5G72S/1G years ago) , &$*&" A ^ & ^+ & <***r ^^r ^ 

Concentration of sherds on the surface^ doc a this vn F y ^ascih^gite? 

(None, Some<l-5 or less per 10m ajeaKgommon (5-10 per IQnTjisa}, Many (10+) 

Concentration of sherds In the sect io nl s) 

None observed, 1-5 per m A 2, 5-10 per m A 2, 10+ per m A 2 

Other material culture observed, describe: _ *? fJ^*J^|*'t«- \ ti 

Surface soil matrix/composition description and M unsell number: 

1 J 


(Boulders 26cm+, Cobbles 6*25cnv Pebbles O-S-ocm, AMOUNT none, some common, many] 


(none, some, common, flecks, twigs, ash/bum layer) 


(none, rare, common, numerous, types) 

(map and describe, if possible) 
Other samples (e.g., copper) 
Artefacts observed 

Figure 7.15 Middle Egypt Survey workbook, photograph by Sarah Parcak. 



(if available), this will facilitate directions to the site and surface recording of features 
and material culture. The next page represents the main recording page, treating the 
archaeological site in a similar fashion to archaeological loci (each included section is 
discussed below). The third page is intended for recording specific comments about 
the site in question, while the last page facilitates sketching material culture collected 
during the survey. 

The following section describes each subsection on the survey description page, which 
can be augmented further by each survey team. In some cases, data can be circled for 
ease of data recording. In all cases, nothing should be too unimportant to note. One 
might assume they will remember a seemingly unimportant observation later on in the 
same day, only to realize later it was a significant piece of data that was omitted as 
other sites and new data required entry. In the lab several months later, one might be 
asking why certain sites appear on the satellite imagery, only to realize for example that 
critical information regarding vegetation patterns was not recorded. Thus, consistency 
and detail remain the most important parts of ground-truthing recording, applying form 
recording sheet numbers similar to archaeological site forms. Recording details are also 
dependent on the time one has to survey a particular site or feature. Naturally, maximum 
information retrieval using the best possible methods is needed, but the limitations of a 
particular survey, or day, should also be described for later acknowledgement of survey 
limitations. The team may not be able to collect data, or may not be able to analyze it in- 
situ. Archaeological sites might also not be able to be photographed due to neighboring 
military restrictions. Some combination of the above situations might be possible on 
any given day. A team may not be able to reach a site or region. Additional sites or 
features might be found on the ground, or be shown to a team by local individuals, 
thereby throwing off the survey timing (unless teams have built in extra time foreseeing 
delays). Each subsection is described in full, noting the particular data one might record 
and why. 

Date I time of day visited 

While dates are always essential to record during archaeological work, time of day is 
equally important. This enables subsequent analyses to correlate what the team viewed 
on the ground during a certain time of day with the specific time at which the satellite 
imagery was generated. Every satellite image is taken at a set time of day, and it may 
emerge that certain features are viewable only during that time. For example, mud- 
brick architecture in the Middle East is relatively easy to distinguish from silt deposits 
in the summer (when many teams work in the field) until around noon, due to the 
ability of mudbrick to absorb moisture. In the afternoon, the ground on multi-period 
archaeological sites tends to become quite hard and dry, thus rendering the mudbrick 
architecture more difficult to view unless one has a practiced eye. Quickbird satellite 
images are taken at 10.40 a.m. each day coinciding with the optimum viewable point for 
mudbrick architecture. Thus, for site features detected by Quickbird imagery, a survey 
team would be best advised to visit them in the morning hours. Conversely, some site 
ground features may emerge best in the afternoon outside the viewing period for satellite 
imagery. Additional remote sensing algorithms may make such features clearer. Addi- 
tionally, one might coincide the time of day spent at each site with the digital photos 
taken. It is too easy to mix up photos from similar sites after weeks of survey work, while 



pre-arranging site and image timeframes aids in keeping editing photographic files for 
each site. 

Site name(s) — ancient and modern 

Most town names change over time. If a team is lucky enough to have the ancient name of 
an archaeological site in question, it can be pre-recorded on forms for in-field comparison 
to historical name changes. Specific archaeological sites are sometimes still called by their 
ancient names or their derivatives by local people, or a related variant. Archaeologists 
may recognize a site by a ancient particular name, but it is essential to realize most local 
people have an entirely different and usually modern name for it. Knowing both ancient 
and modern toponyms is essential for excavation at the site/feature. 

Original GPS points! site directions 

This represents the eastings and northings of the site or feature, ideally reflecting a 
central point, taken during the remote sensing analysis. Aiming for a central point 
maximizes the location of a site on the ground using a GPS unit with a +/- error range. 

Actual northings and eastings 

Satellite images can be incorrectly georeferenced, or can sometimes be georeferenced in 
relation to old maps with inaccurate town coordinates. These coordinates can be later 
corrected with specific ground control points taken at the features/sites in question. 
Ideally ground-control points should be taken of large, unmovable, and "permanent" 
features, such as at a corner of a school, a local monument, a church or mosque (if 
permitted), a large landmark like a factory, or a cluster of structures on or near the 
archaeological site. 

Original published information about site 

In some cases, there may be existing published data discussing the sites or features 
in question. Not all archaeological remote sensing covers previously unknown sites: 
Many archaeologists have conducted large-scale surveys where little is known about 
an archaeological site other than assessments such as "possible site(?)." In various state 
archaeology registers, there may be incorrect data on the current state of an archaeological 
site. All of the known data on the site should be collected in a database format for 
comparison with the satellite imagery analysis. Past comments may aid in interpreting 
ground-truthing data. 

What mapsl surveys does it appear on, if any 

Some areas of the world have been extensively ground-surveyed without the use of satel- 
lite imagery, and require revisiting. Others have not been surveyed, but are in regions 
well covered by modern maps (Figure 7.16). Every government- or foreign-produced 
map will be different, and may emphasize alternative features depending on the map's 
focus (e.g. farm boundaries; road maps, etc.). Gathering as many maps as possible of 



Figure 7.16 Modern Delta maps, photograph by Sarah Parcak. 

the region in question will aid in the remote sensing analysis, especially concerning 
vegetation and geology. A number of maps show archaeological sites/features that pre- 
vious surveys have ignored. All the data can be scanned and imported into an ArcGIS® 

How large is the area in question on the ground 

Each type of satellite image records different data, whether it be spectral, spatial, or 
temporal data. Satellite images from different times of year may show slightly different 
data (Parcak 2005 , 2007a), so the actual size of the sites/features on the ground may differ 
from the original analysis. How and why the ground-data differs is also an interesting 
discussion point, and will help in later analyses. 

How does this compare to the different satellite image information 

Each study will rely on a variety of satellite imagery, and it is useful to discern how 
each image type portrayed the archaeological site/features compared to what the team 
observes on the ground. 

Towns and cities closest to site (and location in relation to these sites) 

This category is also crucial. As some modern maps have been shown to be unreliable, it 
is helpful to have actual directions to the archaeological site or feature from the closest 
towns. If one becomes lost during the survey process, does not have access to maps, or 



there are issues with a GPS unit, a set of directions can remedy this situation. Published 
directions for future teams or government organizations are also helpful (e.g. take the 
large road heading north from such and such a town, take the second right to town Y, 
and then take first left. The site is approximately 250 m NW from the turnoff). 

Who owns the land on which the site is located 

Many archaeological sites and features may be located on privately owned land. If possi- 
ble, it can be useful to have the name of the individual community members who own 
the land in question. Inquiries can be made of the landowners to see what additional 
archaeological features may have been visible in the past. Any inquiries should be made 
with caution and discretion since it may be illegal in some nations as digging and may be 
regarded as destroying an archaeological site, albeit through plowing or other indirect 
activity, or a government may be able to seize the land. This is a sensitive issue and 
should be dealt with carefully. 

Shape of site or what is left (if anything) 

Things can change quickly on the ground, even over the course of several years (i.e. the 
time-span between the date of the satellite imagery and the subsequent survey). Features 
noted in the generated satellite image data may no longer exist by the time the survey 
is conducted. It is also possible that additional features may have emerged depending 
on the time of year in which the survey is conducted. Even if a site is "destroyed" (i.e. 
usually being levelled to modern ground level), cropmarks may still indicate significant 
subsurface features. The actual surviving shape of the site or feature is important to 
describe, and plot (via a GPS) on the ground. It may differ greatly from what appeared 
during the satellite imagery analysis, and the remote sensing team needs to address why 
and how. For example, crops may cover a large part of a site, obscuring it from space, 
but a slightly higher crop or topographic elevation may make a mostly subsurface site 
viewable at ground level. 

Description of local vegetation 

This section facilitates detailed discussions of the surrounding field systems (if any). 
Dialogues with farmers or landowners will aid in this process. Crop patterns can change 
each year via crop rotation, and will affect how features do or do not appear in the 
satellite imagery. Fertilizers in the soil may also affect the spectral signatures of land- 
scapes, but would yield a widespread and more field-boundary aligned signature than 
an archaeological site. Certain types of vegetation will grow on top of archaeological 
features in different parts of the world. For example, in coastal areas of Costa Rica, as 
shown by student research as part of the author's introductory remote sensing course, 
palm trees are drawn to shell middens, due to the large concentration of calcium car- 
bonate in the soil. In particular circumstances, concentrations of these trees detected 
by satellite imagery may tend to indicate the presence of shell middens (pers. comm. 
B. Arnold, April 2008). Archaeological soils and associated structures also influence 
the spectral signature of imagery. Other patterns and association with archaeological 
remains undoubtedly exist in other parts of the world. 



7.17 Tanis, Egypt (scale 1 : 1000 m), note extensive cropmarks to the east of the site, 2008 
image courtesy of Google Earth™ Pro. 

Can you see crop marks or any shape or size on the ground 
surrounding the site? 

This is more specific to the archaeology of crop regions. Walls thinner than 60 cm 
fall below the pixel size detected with high-resolution Quickbird satellite imagery 
(Figure 7.17), may or may not appear on satellite imagery at certain times of year. 
Crop rotation and vegetation growth patterns change each year. Cropmarks appear most 
clearly during the initial growth phases of grass and crops due to thermal immersion 
patterns (Shell 2002). Hence, timing one's tasking of satellite imagery to the optimum 
time of year to detect features through crop marks is crucial, but may not be realized 
until the initial investigation season. However, once a team is fully attuned to their 
study region, future imagery purchases can be adjusted accordingly. 

Are there any architectural features on the surface of the site? 

Features observed in the satellite imagery should be correlated with anything observed 
on the ground. Some circular, spiral, rectilinear, or other-shaped features may appear on 
satellite imagery but not on the ground, or vice versa. This may reflect the time of year 
of the imagery used, weather patterns, the period of the survey, or the general wetness or 
dryness of a given feature or site. The specific time of the survey may need to be adjusted 
to maximize recording capacity. 



What are the irrigation systems or current! past water sources 

surrounding the site (describe in detail: Size of canals I lake si rivers, 

amount of water, when it I they are high or low, when is the area dry)? 

Water is the primary source that affects how archaeological features can be viewed from 
space. Many features show up in either wet or dry periods of the year, and knowing 
in what seasons soil and other surface coverage may be affected by relative moisture 
levels will assist in further imagery analysis. Human influence is another key to water 
source management. Governments may release water from dams at set times of year 
(e.g. Aswan). This should be noted during ground survey work. The overall size of water 
sources or former (or current) water sources may also affect how archaeological sites can 
be viewed. With lake and river beds in desert regions drying up over long periods of 
time, archaeological sites can often be found by examining concentric rings of former, 
higher, lake edges (Figure 7.18) (Bubenzer and Bolten 2003), or the banks of former 
rivers (Wendorf et al. 1987). Additional deep wells or dense vegetation may also indicate 
former water sources and thereby associated archaeological sites (Mumford and Parcak 

Where is the highest point of the archaeological site? 

Higher parts of archaeological sites generally indicate areas of better preservation, or 
denser archaeological debris. Surveying the site with a differential GPS will allow for 
a 3D rendering of the area, which the imagery specialist can then use for draping over 

Figure 7.18 Kharga Oasis, Egypt (scale 1:410 m), 2008 image courtesy of Google Earth™ Pro. 



the satellite imagery. Further imagery analysis in the higher area may reveal additional 
features or signatures worthy of investigation. 

What is the concentration of sherd(s) for each area? 

If an archaeological site is covered with a sufficiently dense quantity of potsherds (e.g. 
Umm el-Qaab at Abydos, Egypt), these could affect the signature of the site. No previ- 
ous remote sensing publications appear to discuss this factor. Sherd(s) may reflect more 
strongly in a particular part of the spectrum or may be viewed in a similar fashion to 
geological features. Observing and testing the signatures obtained from such concen- 
trations of ceramic material within or covering archaeological sites will aid in refining 
the satellite imagery analysis. On higher- resolution imagery, sherd clusters may not 
have a significantly different spectral signature, but may have their own digital number 
range, which may vary slightly from the rest of the image to an extent where they can 
be isolated and pinpointed on other archaeological sites. This may work particularly 
well in imagery programs where individual pixels can be isolated outside of running 
classification algorithms. 

What other material culture remains can be observed? 

Isolating additional material culture remains may aid in interpreting satellite imagery, 
especially higher- resolution imagery. For instance, subsurface metal working areas, ovens, 
kilns, middens, and simple graves in cemeteries may yield their own spectral signatures 
within a site, due to the different ways they affect the soil. 

What does local knowledge have to say about the site and its history? 

Consulting indigenous records, officials, and private individuals near or on site may aid 
in elucidating further details and local traditions of one's study region in contrast to the 
published archaeological data. 

Site photograph notes 

As many features as possible should be digitally photographed, with description and 
locational comments added to each image entered into the field computer at the end 
of each workday. Having a catalogue of local vegetation types, soils, and geology will 
aid in later analyses of the satellite imagery, and it is also helpful to have a running 
photographic record for later comparative purposes (Figure 7.19a and 7.19b). Taking 
video footage of the area, perhaps panning slowly for 360 degrees from the site center 
(when possible), will also aid in later analysis, as it can help the team both to document 
and "revisit" the site, allowing additional features to be noted photographically. 

What preliminary observations can be made about the sherd(s) and 
material culture remains in an area? 

Initial thoughts by a team about the ceramic material or material culture, in general, 
will aid in forming a more definite impression of a site's nature, date, span, and other 
aspects. It is recognized that much early observation can be speculative, while firm 



Figure 7.19 Kom el-Ahmar, Middle Egypt: (a) Quickbird image (scale 1:200 m); and (b) photo- 
graph, note the density of the pottery scatters on the site surface, darker shaded areas 
on the Quickbird image represent these sherd scatters, images courtesy of Google 
Earth™ Pro (2008) and photograph by Sarah Parcak. 

conclusions are not usually realized before conducting rigorous archaeological analysis 
(i.e. drawing sherd(s), cutting thin sections, conducting lab analyses, radiocarbon dating, 
and analyzing material culture from secure contexts). It may be more helpful to formulate 
a series of questions about the newly found site or feature (e.g. "From surface material 
culture, the site appears to be occupied only during the Old Kingdom — is that the case 
for all sites in the region?"). Additional research is generally necessary in most cases when 
one locates an unusual feature or site. Considering various questions on the ground at 
the newly located site will further the research agenda, since many questions will need 
to be answered at the site and cannot rely on memory after departing the site. This is 
where it is advantageous to survey with several members, to general dialogue and foster 
as many questions and answers as possible. 

Example recording sheet (from 2004 Middle 
Egypt Survey Project) 

Site Number/Name: Tell Sheikh Masara 

Date: March 6 

Eastings/Northings of Site on Landsat: 2853205 

(note: 1 km + offset included in each reading) 3063285 

Near Villages (Names — ancient/modern): Mallawi 

Original Published information about site: None previously known 

Found on Maps: 1920s Map, Mallawi 

Found on what Corona series: Corona 1111 series, 1972 

How large is the area in question on the ground: 80 x 80 m 

How does this compare to the different satellite image information: 

Site size (Landsat): 90 x 90 m (small pixel cluster) 

Site size (Corona): Small (does not appear to be much larger than 90 x 90 m) 



Towns and cities closest to site (and location in relation to these sites): 

Located in fields 2 km south of Mallawi 

Who owns the land on which the site is located: Not known, inquiries 

to farmers did not yield any answers 

Description of local vegetation: To the east, sugarcane and fields, to the 

south clover, to the north and west more sugarcane 

Shape of site or what is left (if anything): Semi-square 

Can you see crop marks or any shape or size on the ground surrounding 

the site? None seen surrounding site 

Are there any architectural features on the surface of the site? Old brick 

structure, probably modern 

What are the irrigation systems or current/past water sources 

surrounding the site (describe in detail: Size of canals/lakes/rivers, 

amount of water, when it/they are high or low, when is the area dry)? 

None observed save modern irrigation channels 

Where is the highest point of the archaeological site? Sheikh's tomb and 

modern bricks 

What is the concentration of sherd(s) for each area? Mixed 

concentration, denser concentrations next to modern pits and around the 

edges of the site 

What other material culture remains can be observed? Silty surface, 

some pottery, modern mudbricks, and halfa grass, not much else on surface 

What does local knowledge have to say about the site and its history? 

One man said: "Since my grandfather's time the site has been like this, but 

much bigger, many feddans larger." 

Site photograph notes: Photo numbers 1-18 for the day. 

What preliminary observations can be made about the sherd(s) and 

material culture remains in an area? 

The survey went to visit an old sheikh's tomb and it turned out to be on 

top of an ancient tell site, initially discovered in the remote sensing analysis. 

It rises 4m above the surrounding fields, and is covered in halfa grass, dried 

sugarcane and several piles of sherd(s) that likely came from the robbers' 

trench in the middle of the site. This trench also reveals a corner of a red 

brick structure. The sherd(s) seem Late Roman in date, with one potential 

Islamic sherd seen. It once was a very large site, as many sherd(s) were seen 

in the fields walking to and from the site. Another sheikh's tomb on the 

site had collapsed. According to locals, the sheikh's coffin was burned, but 

many people had come here because it was a "sacred area." The pottery dates 

to ca. ad 400-800. 


Ultimately, when doing remote sensing survey, it is the number and appropriateness of 
questions one asks about the archaeology and surrounding landscape that generate the 
most useful data for the post-processing stage. As a survey is ongoing, all completed 
archaeological sites and features should be noted and mapped according to the spatial 



Table 7.1 Survey times based on relative town sites during 2003/4 Egypt surveys 

Relative town size 

Time per site 


Time total (h) 

Time in months 

85 villages 


85 hours 


40 small towns 


200 hours 


20 medium towns 


200 hours 


10 large towns 


200 hours 


5 cities 


200 hours 


160 towns 


685 hours 

5 months, 4 days 

and temporal subdivisions of the survey. Additional logical places for site locations 
(e.g. predictive modeling) that may not appear during the imagery analysis should 
be included, such as caves or overhanging rock formations. Site deflation occurs in 
desert regions, while floodplains and mound formations may bury and obscure earlier 
archaeological site levels from imagery analysis. Each landscape type, whether it is 
desert, jungle, floodplain, urban, coastal, forested or mixed, offers diverse "typical" 
global environments. These types and the work conducted within them will be affected 
by local conditions, time of year, and the experience of the remote sensing and survey 
teams, vary markedly. 

Given the broad results one can obtain from combining ground survey and remote 
sensing, and the confusion which can result from landscape destruction, it appears that 
the entire field of ground survey and field- walking needs to be reconsidered. A study 
carried out in the heavy clay soils of South Tuscany compared remote sensing, aerial 
photography, ground survey and excavation (Campana and Francovich 2005). It noted 
that an integrated approach was needed due to issues of landscape interpretation and 
recording: 50 percent of Tuscany is covered in forests, while the rest contains cultivated 
heavy soils. In addition, the authors observed that the best areas for visibility in South 
Tuscany represent alluvial plains, but encountered problems with alluvial thickness and 
development. The study also recorded major differences between surface collection and 
excavation, with two sample areas of 470 km , using oblique aerial photographs and 
Quickbird and IKONOS imagery. Using the IKONOS imagery, the team located 84 
features on the ground, with 39 new sites appearing. Older aerial photographs revealed 
some features that could not be observed in the present day due to poor IKONOS 
imagery. Anomalies appeared in the infrared part of the spectrum only. The capture 
time of the satellite imagery proved to be an issue, with a delay of five to six weeks. 
Quickbird imagery covering an area of 200 km , with a delay of 15 days in acquisition 
time, was also used for feature detection. The team noted that one pixel of multispectral 
IKONOS imagery equaled 32.65 pixels of Quickbird pan-sharpened imagery. One way 
to augment these results would be to pan-sharpen the IKONOS imagery, or priority 
order their Quickbird imagery (with only a delay of 24 hours). 

Widespread destruction of landscapes via growing settlements, agricultural holdings, 
and other projects, has become an archaeological surveyor's nightmare. Archaeologists 
must reconsider the distinct processes that are affecting site destruction and encroach- 
ment in different parts of the world, and incorporate these areas and forthcoming 
threatened areas to the areas one hopes to evaluate using satellite remote sensing anal- 
ysis and ground survey. Destructive processes change over time, and will vary given 



the amount of alteration each landscape has undergone in the past thousands of years 
(depending on a survey time period). The team in Tuscany used mobile PDAs to con- 
duct a real-time comparison by creating a real-time virtual landscape, but noted many 
data collection problems. The team recorded 1000 sites on 10,000 oblique photographs, 
with an additional 9000 sites recorded in the field, but had significant difficulties in 
interpreting the data as it was incomplete with post-depositional processes obscuring 
many areas. Observing things on the ground during survey work would have made for 
an incomplete landscape analysis. Remote sensing, however, allowed the team to eval- 
uate past and present land-use patterns, to see how broadly modernization had affected 
landscape usage. 

Archaeological survey and remote sensing can no longer be considered as disparate 
entities. Globalization requires more time- and cost-efficient ways of conducing surveys 
in areas threatened by development and looting, while global warming is becoming 
a significant threat to field research, especially in coastal areas. How many archaeol- 
ogists can claim that the excavations and surveys they conduct today are not salvage 
work? Satellite remote sensing can contribute immeasurably to the process of survey 
and site preservation, while remote sensing in and of itself should not be conducted 
in a vacuum. Both approaches require new perspectives and new strategies, which will 
continue to be refined as satellite imagery and techniques of analysis improve. Addi- 
tional resources must be created to address the issue of communicating remote sensing 
survey strategies, whether through blogs, list-serves, annual symposia, or other media. 
As the world changes even more rapidly, archaeologists will need to devise improved 
surveying strategies no matter where they are working. Considering the full range of 
archaeological remote sensing applications to survey provides a good start. 







Remote sensing in archaeology should not be considered from the sole perspective of 
archaeological site location. Studying any features and landscape changes from space 
necessitates a broader perspective, and one that addresses long-term issues with global 
implications. "Preserve the past for future generations" is an oft-repeated message in 
archaeology, yet how does remote sensing fit into longer-term planning for site and 
feature preservation? It is a difficult question to address, especially considering how 
remote sensing has become a public activity with global participation possibilities. As 
remote sensing is at a crossroads of many fields and subfields, remote sensing analysis can 
detect the effects of tourism, development, and global warming at archaeological sites 
around the world. Natural and non-natural factors affect archaeological site preservation 
simultaneously. Archaeological remote sensing can detect both of these factors in tan- 
dem so archaeologists can plan future management strategies. This is not an easy task, 
and is becoming more difficult with rapid globalization, thus necessitating alternative 
strategies that should include local remote sensing training initiatives and better data 
sharing practices. 

The very nature of archaeological remote sensing data makes remote sensing ethics 
and the law a central point for debate and discussion. Do the ethics of remote sens- 
ing fit within the debate over archaeological ethics, in particular the ethics statements 
adopted by the Society for American Archaeology (SAA), the Archaeological Institute 
of America (AIA), and other learned archaeological societies around the world? Archae- 
ological remote sensing discourse necessitates an alternative ethics statement, and one 
that brings together archaeological and remote sensing ethics. 

Education is another critical factor with archaeological remote sensing, considering 
the usage of Google Earth™ by millions of people around the globe. Archaeological 
discovery and exploration are no longer restricted activities: When people first download 
Google Earth™, the first places generally visited (following one's own home) include the 
Giza Pyramids, Machu Picchu, the Great Wall of China, and the Taj Mahal (Figure 8.1), 
creating a form of desk-based tourism. Amateur remote sensers have located numer- 
ous "strange" anomalies, which are often communicated to professional archaeologists 
via email. In a recent National Geographic News article (Handwerk 2006), archaeolo- 
gist Scott Madry discussed how he had located Roman-period villas on the ground in 
France, communicated to him by French people who found them using Google Earth™. 



Figure 8.1 Taj Mahal, India (scale 1:180 m), 2008 image courtesy of Google Earth™ Pro. 

Google Earth™ is now in the classroom, and also available to looters, who have most 
likely incorporated it into their looting plans. 

Education is the only way forward with regards to responsible archaeological usage 
of these emerging technologies (Kligman 2006), but until governments make heritage 
and conservation national priorities, and implement stricter policies for the sale of all 
antiquities, the looting will continue. Pressure from universities, museums, public and 
private groups around the world seems to be working in some cases: Recent government 
crackdowns have led to the arrest of several high-profile antiquities trackers, such as 
the US case against Fredrick Schultz involving Egyptian antiquities (and a subsequent 
conviction under the United States National Stolen Property Act) as well as the ongoing 
Italian case against Getty Museum curator Marion True. This puts a higher priority on 
educating the public and students in the ethics of using remote sensing, as all public 
information regarding newly discovered archaeological features is probably examined 
in-depth by those who are destroying the archaeological record. 

While governments do their best to guard known archaeological sites and lock 
museums, looters rob poorly guarded known sites and non-guarded unknown sites 
every day, thus fueling the international illegal antiquities trade. How archaeologists 
can detect, protect and preserve the tens (if not hundreds) of thousands of unknown 
archaeological sites around the world to prevent this looting is an ongoing debate 
in archaeology, especially regarding resource management. Satellite remote sensing, 
in combination with aerial photography, allows, at present, for the fastest detection of 
larger archaeological sites across broad landscapes, due to the presence of many shallowly 
buried ancient features that enrich sites with organic or magnetic debris. However, once 
archaeologists publish remote sensing results, looters can purchase their own satellite 



imagery and attempt the same methods of analysis. Having the resources to ground-truth 
the located sites and features, as well as protect the sites, from such looters is another 
matter entirely. Governments should place heritage higher on their list of priorities: 
Archaeological heritage is closely tied in with national pride, and more interest in the 
heritage of a particular country can bring in tourist money as well as money from foreign 
archaeological expeditions. Ecotourism (which includes visits to archaeological sites) is 
a thriving industry, and having more identified and secure sites in beautiful locations 
will aid local economies. The employment of local people on archaeological expeditions 
is another way to improve the economies of poor villages and towns. Ultimately, work- 
ing with foreign governments, if possible, to train their archaeologists as well as learn 
from them, is the best way to implement good archaeological remote sensing practice. 
This encourages good archaeological practice, which aids in conservation and heritage 
management initiatives. 

The conservation of archaeological sites via remote sensing is a multifaceted issue 
affected by both natural and anthropomorphic factors (Williamson 1998; Bertocci and 
Parrinello 2006). Modern landscapes change quickly and at ever-increasing rates, which 
may or may not affect archaeological site preservation. These broad changes affect local 
and regional climates, biodiversity, soil degradation, and general human vulnerability. 
How quickly this change affects archaeological sites can be considered as a separate but 
related issue. Archaeologists should evaluate the overall rate of change per archaeological 
site type and features within a landscape, while comparing it to as many variables as 
possible. This includes the site or feature's distance from population centers, roads 

;■ ' %/J 

Figure 8.2 Karnak Temple, Egypt (scale 1:267 m), note proximity of the temple complex to the 
town, 2008 image courtesy of Google Earth™ Pro. 



(Figure 8.2), agricultural and industrial development zones, and general landscape 
changes caused by weather and natural disasters. The threat of destruction or partial 
removal for certain sites should be explored, especially regarding overall rates of change 
through land use land cover analyses. Using the full range of tools available for the 
remote sensing analysis is necessary but not always possible due to a lack of maps, aerial 
photography, or perhaps high quality satellite imagery. Comparing past and present 
landscapes makes it possible to assess future archaeological landscapes, which may or 
may not be available for more detailed exploration. Identifying, via remote sensing, 
where to work and how much time remains for more detailed archaeological analy- 
sis contributes towards a form of archaeological triage: If archaeologists do not know 
where to look, they cannot even begin to make plans for archaeological conservation. 
Addressing natural changes within landscapes is the first logical step towards conserving 
archaeological sites and their associated landscapes. 

Conservation and heritage management 

Natural forces affecting archaeological site preservation are numerous and vary according 
to landscape type. Change can occur over a single day or over thousands of years. Varying 
types of natural factors affect archaeological sites and features, which is why remote 
sensing can aid in untangling the web of change possibilities. Desertification, rapid 
rainforest growth, flooding, vegetation changes due to global warming or cooling, forest 
regrowth, and earthquakes are but a few of the natural changes that can affect landscapes 
and archaeological sites. Archaeologists can assess entire archaeological landscapes from 
space to prevent natural disasters from damaging monuments, but how much can these 
measures help? In 1994, a massive and unexpected flood hit the Valley of the Kings in 
Egypt, damaging dozens of tomb interiors. Now, the Egyptian government has placed 
floodwalls in front of the tombs of Sety I and Ramesses I (Figure 8.3), yet how well 
these will combat future flooding remains a point of contention. If archaeologists had 
carried out a detailed remote sensing analysis, it is possible that predictive models 
could have assisted in better placing the floodwalls along the desert mountaintops to 
divert the flood waters, such as what Jordanian archaeologists employed at Petra using 
a combination of SPOT imagery and GIS analysis (Akasheh 2002). Only a real future 
flood will test the effectiveness of the measures, but it underscores the necessity of using 
all available technologies at hand to protect major monuments such as the Valley of the 
Kings and other threatened sites. 

Natural disasters can be expected in archaeological landscapes near fault lines, or in 
locations where humans have employed measures to make uninhabitable land habitable. 
New Orleans represents such a location. The San Born Company mapped New Orleans 
over 200 times between 1876 and 1994, making it one of the most well mapped cities in 
the world. One project placed each map as a layer within a GIS to examine overall land 
use and land cover changes (Berry 2003). Now, large parts of the city remain deserted 
following the major flooding in 2006, a situation that is not likely to change in the 
next 5 to 10 years. Archaeologists should learn a great deal from this disaster: Even 
well mapped cities in modern landscapes are quickly abandoned in ways that cannot be 
readily understood without examining local land management practices. How and why 
parts of the city remain inhabited can be tied in with complicated social, economic, and 
political factors, which, outside of direct natural affects, remain at the heart of landscape 



Figure 8.3 Valley of the Kings, Luxor, Egypt (scale 1:171 m), 2008 image courtesy of Google 
Earth™ Pro. 

changes. Human inhabitation in these high-risk zones provides clues to remote sensing 
specialists to how and why groups may have abandoned areas or returned to them. 

Past maps may be the only resource available with information about landscapes 
affected by natural and anthropomorphic factors. In Germany, the Danube Valley repre- 
sents a landscape largely altered by these factors. Archaeologists assessed the Heuneberg 
early Iron Age topography over a 20 km area using LIDAR, creating a detailed digital 
elevation model (DEM). They located a number of mounds and some rampart systems 
evident on an industrial map from the 1920s, but did not locate a ditch still visible in 
the same map (Bofinger et al. 2006). This clearly shows that modern disturbances had 
long since eroded the ancient landscape, and that even the most advanced remote sensing 
systems are not capable of detecting the entirety of disturbed landscapes. Ancient texts 
such Herodotus (reprint 2003) can aid in either relocating lost sites or providing details 
about the destruction of cities, but these are restricted to larger cities in historic times. 
Approaching damaged landscapes dating to non-historic or non-literate times can prove 
even more difficult. 

Areas under threat from natural disasters or anthropomorphic affects can be mapped 
with high-resolution imagery, but the decision to protect a region resides with the 
national government or international bodies. India has several examples of these sites. 
The Agra, or Taj Mahal, is one of the world's best known architectural achievements, 
yet it remains under threat from pollution. Remote sensing specialists used IKONOS 
imagery to classify the land cover surrounding the Taj, and have proposed the area to 




if i 

' § 


Figure 8.4 Southeast India in Tamil Nadu along the Mahabalipuram, Landsat image, image 
courtesy of NASA World Wind. 

UNESCO as a heritage zone (Dayalan 2006). Larger areas already classified as a world 
heritage site, such as the coastal system in southeast India in Tamil Nadu along the 
Mahabalipuram (Ramasamy et al. 1992), shown in Figure 8.4, dating to between 600— 
700 bc, face constant threats from both biophysical and anthropogenic effects. The river 
system is 1000 km long, composed of 46 rivers with a catchment system of 171,000 
km , populated by 5.8 million fishermen. There are significant problems arising from 
urbanization, overpopulation, tectonic changes, sea level rises, and coastal erosion from 
natural factors (Krishnamoorthy et al. 2002). How can international bodies with limited 
resources such as UNESCO protect such a large area with a dense population? The issues 
extend far beyond the capabilities of a single organization. A single archaeological site 
can be guarded, while an entire heritage zone requires much broader thinking and 
planning when thinking about longer term management plans. 

Landscapes and archaeological sites within those landscapes can be altered to the point 
where remote sensing analysis can no longer detect their existence. Archaeologists have 
long argued that no matter how destroyed an archaeological site might be, there will 
always be a way for archaeologists to detect its existence, whether through geological, 
chemical, or technical analysis (Renfrew and Bahn 2008). Do we need to reconsider this 
statement? Modernization has altered landscapes to the point where advanced remote 
sensing analysis via high-resolution imagery and Airborne P-band SAR can no longer 
detect the presence of known archaeological sites (Chapoulie et al. 2002). Ground cover 
and weather patterns combine to obscure ancient sites, which may not be detectable via 
ground-based remote sensing techniques due to issues with timing and cost. Even the 
speed and character of overall site degradation is only beginning to be understood. Using 
a combination of Quickbird and IKONOS satellite imagery and chemical sampling (i.e. 
finding phosphate, iron and zinc levels in the ground), a team sampled a 1 x 1 1 km area 



in Rygge Municipality, Norway, over a flat and hilly landscape. The team compared the 
error in detected anomalies versus known anomalies, finding likely archaeological sites 
versus "real" sites. Although the study located 30 previously unknown archaeological 
sites (mounds and some ancient houses), only 458 sites appeared during the survey, 
which the team estimated to be 1000 too few, with many thousands of additional 
likely sites not detected (Gron et al. 2004). How much people transform landscapes can 
only be understood using both chemical and air-based methods as entire archaeological 
landscapes are lost entirely each year. How much more is lost with the construction of 
towns, and accompanying pipelines, roads, buildings, factories, as well as mechanical 
plowing, needs more study, before landscapes can no longer be characterized by any 
archaeological means. 

Countries such as China undergoing more rapid development have recognized the 
heritage and conservation-related problems that go along with urbanization. Environ- 
mental problems in China are also coupled with a major rise in tourism. The Chinese 
Center for Remote Sensing in Archaeology has studied this problem in detail, and had 
found a contradiction between conservation and utility development. Although sites 
can be affected by development, they are also aided by the general improvement of a 
region (i.e. areas can better serve tourists and archaeological teams) (Shupeng 2004). Dif- 
ficult questions remain surrounding the development of Beijing for the 2008 Olympics. 
Dozens of historic hutongs no longer exist due to the increase in number of 4- and 5 -star 
hotels, and one wonders what lies beneath the city, much like in Rome or Athens, which 
is now lost to archaeological exploration. Similar development issues have affected the 
archaeological remote sensing exploration of the Great Wall and nearby mausoleums. 
Out of the 600 known mausoleums, only 113 can be found with remote sensing, while 
one-third of the known Ming wall sections have disappeared. Over the past hundreds 
of years, locals have removed bricks from these sections for houses, roads, and pig sties 
(Shuren et al. 2004). Laws protecting archaeological sites need to be followed on a local 
level, but laws (if in place) do not always mesh with development needs. Changes in 
politics and government policies become just as important to study as landscape changes 
and history. 

How global warming affects population movement, crop cycles and general 
governmental environmental decisions is closely tied into heritage and preservation 
issues. In Central America, the production of milpus, an important crop, is linked to the 
global climate: A hotter climate shortens the dry season, thus preventing slash and burn 
agriculture, while a cold front delays the wet season planting (Gunn et al. 1995). Farm- 
ers using slash and burn agricultural practices have destroyed large tracts of rainforest 
across Guatemala, exposing archaeological sites to looting (Saturno et al. 2007). How 
do similar practices affect archaeological landscapes across the globe, and how can they 
be detected with remote sensing analysis? Many archaeological landscapes located in 
dense forested areas remain to be discovered, including numerous Islamic cities located 
along the east Africa coast, seen in Figure 8.5 (Chittick 1974). Loggers and developers 
are unlikely to report finding a city in the jungle, as it would only delay construction or 
shipment dates. Poor education contributes to this destruction: Deforestation in rainfor- 
est is often illegal, with those who practice slash and burn agriculture not realizing that 
sustainable practices would support their families to a far greater extent (Martini and 
Souza 2002). With changing climates, countries employ alternative agricultural prac- 
tices, thus further altering landscapes. In addition, millions of people are now displaced 



Figure 8.5 Takwa, Kenya (scale 1:77 m), an important coastal trading site, 2008 image courtesy 
of Google Earth™ Pro. 

persons due to war and famine. How can we justify the intersection between human 
need and protecting the past? 

This debate extends to the construction of dams. Rivers with unpredictable annual 
flooding can be dammed (with accompanying social and political issues), but the cost 
to archaeological heritage is enormous. Any time large tracts of land surrounding a 
water source that has been in use for thousands of years become submerged, it is log- 
ical to assume that hundreds or thousands of archaeological sites will disappear, many 
of which will not be known. Archaeologists and anthropologists have fought against 
the rising waters of the Three Gorges Dam project, and dams across the Middle East 
(especially the Ataturk dam in Turkey), to record sites before their submersion. In 
the case of the Aswan High Dam in the 1960s and 1970s, a call from UNESCO 
prompted an international team of archaeologists to aid in the complete excavation of 
large number of sites (Adams 1979) or the removal of temples to higher ground. Sim- 
ilar dam construction in other countries has not met with the same broad international 
efforts. Archaeologists have studied how dams have affected landscapes using Landsat, 
SPOT, and Corona satellite imagery (Comfort 1998). One study examined settlement 
change in the late Neolithic in the Murhgab River Delta region of Turkmenistan over 
a 20,000 km area using Landsat and Corona satellite imagery. The team found that 
a dam constructed following World War II had destroyed many archaeological sites, 
with subsequent irrigation works and desertification combining to eliminate additional 
sites from the landscape (Cerasetti and Massino 2002). Although surveys are ongoing 
in Sudan's Fourth Cataract region, where the Sudanese government is constructing a 
dam along the Nile, archaeologists are not using remote sensing to model or map the 



landscape, instead using foot survey to map the thousands of likely unknown sites, 
which will soon be underwater. These issues extend far beyond heritage to political 
disagreements over water, which will become a bigger problem as global warming 

If developers have caused deforestation or destruction in a known archaeological land- 
scape, archaeologists may benefit from studying these landscapes before further changes 
occur. Forested landscapes in Europe can be studied with LIDAR (Doneus and Briese 
2006), but this is not feasible over large areas due to cost. Bare landscapes may reveal 
archaeological features if non-mechanized farming is in practice. Using Landsat imagery 
from 1987 and 2002, one team examined 24 Iron Age burial cairns in Scotland using 
NDVI, and found higher NDVI values in the later imagery (Barlindhaug et al. 2007), 
showing denser vegetation. Some countries have strict forest regrowth policies, or have 
implemented stricter policies over the past 20 years. These policies should be examined, 
as vegetation and archaeological site discovery are closely connected. Each country will 
also have specific policies dealing with archaeological site protection: Many governments 
have specific policies that dictate an area must be surveyed by an archaeological team 
prior to land development. The policies will differ per country and many laws may not 
be followed. 

Archaeological site management and conservation plans should reflect the many ways 
natural and anthropomorphic affect landscapes surrounding archaeological features. Each 
landscape will present a different set of problems. SPOT and Landsat satellite imagery 
have examined marsh reclamation and sedimentation surrounding Mont St. Michel, 
seen in Figures 8.6a and 8.6b (Deroin and Verger 2002), which will continue to worsen 
as sea levels rise. Coastal archaeological sites can be monitored with satellite imagery 
datasets to monitor monthly or yearly changes, which also include tourism. A total- 
ity of approach using environmental data, aerial photography, GIS, maps, education, 
and public outreach remains to best approach to preserve these landscapes (Dingwall 
et al. 2002). 

This is not a realistic approach in the majority of the world's landscapes, especially 
in areas where site guards are poorly paid (if they exist), and where tourists are allowed 
to touch tomb walls or even take away pieces of the monuments. At the Acropolis in 
Athens (as seen by the author), site guards spread marble chips on the ground each 
morning, as many tourists take "a piece of ancient Greece" home with them. How does 
tourism affect the actual spectral signatures of archaeological sites? This is a subject that 
remains to be studied. How general tourism can be monitored from space is not some- 
thing easily studied: Archaeologists and heritage specialists generally rely on number 
counts of visitors to sites. What can be studied, however, is how the landscape sur- 
rounding the archaeological sites has changed, whether through enlarged towns, newly 
constructed hotels, and how much these hotels create pressure on the local ecological 
systems. With ecotourism companies creating trips to more far-flung locations, more 
archaeological sites are under threat of destruction. In locations such as Egypt's West- 
ern Desert, archaeological sites once known only to archaeologists are now covered in 
off- road vehicle tracks, while tourists scatter surface (and often in-situ) archaeological 
remains. Educating tourist companies is now just as important as educating to tourists 
who flock to remote locations. While some companies practice ethical tourism, booking 
only "green hotels" using local products, and promoting good tourism practices, the 
cost is generally prohibitive for the average tourist. 



Figure 8.6 Mont St. Michel, France: (a) 1990 Landsat; and (b) 2000 Landsat images, note the 
better resolution of the later image, images courtesy of NASA World Wind. 

Evaluating significant changes in Egypt's landscapes brought about by tourism and 
natural factors provides a useful case study analysis for comparative purposes. Tourism is 
the main economic source of the Egyptian economy, so preserving and conserving archae- 
ological sites is of interest to the Egyptian government. However, population growth 
at over 2 percent per year, combined with major urbanization and coastal development, 
pose significant threats to Egypt's archaeological sites. Remote sensing studies outside 



archaeology have contributed to our understanding of how quickly these changes are 
taking place. Egypt's population is projected to reach 111 million people by 2030, with 
90 percent occupying less than 5 percent of the land, chiefly in the Delta and along 
the Nile (Sultan et al. 1999). Landsat satellite imagery has shown that cities and large 
villages in the Delta grew 37 percent in size from 1972—1990, while small villages grew 
77 percent, with an overall urbanization rate of 58 percent (Sultan et al. 1999). Urban 
expansion represents an even greater threat to archaeological site preservation. The over- 
all area occupied by cities and towns reflects a doubling of urban areas every 20—30 years, 
as shown by Landsat MSS imagery, and with a constant growth rate urban areas could 
cover 12 percent of the Delta by 2010 (Lawrence et al. 2002). It is most concerning that 
the majority of unknown archaeological sites in Egypt are located next to or beneath 
smaller villages and towns. Changes in sea levels and the increasing salinity also affects 
many sites in the Delta, along with accretion and erosion along the Delta's coastline, 
analyzed using Landsat and SPOT satellite imagery (Frihy et al. 1994; El-Raey et al. 
1995; Frihy et al. 1998). Coastal development along the Red Sea is another problem. 
The Red Sea coastal zone is a unique natural resource, with both natural and archaeolog- 
ical resources under threat from development (Peacock 1993). The Eastern and Western 
Deserts cover such large areas, the Egyptian government cannot protect them from loot- 
ers who regularly deface or cut away Pharaonic or prehistoric rock inscriptions (Darnell 

Additional environmental problems in the Delta and Nile Valley include increased 
salinity, shorter fallow periods, loss of alluvial deposits, and no nutrient replacement 
(El-Baz 1989; El-Raey et al. 1999; El-Gamily et al. 2001). These factors explain why so 
many archaeological sites are threatened: Farmers use the nutrient-rich archaeological 
site soils with high phosphate levels as fertilizers, to meet rising needs for cultivation 
(Lenney etal 1996). Often, brick factories (which use archaeological debris to create mod- 
ern bricks) can be located atop archaeological sites, with up to 10 brick factories observed 
on one site (Van den Brink 1987). On a local level, officials remove non-registered 
archaeological sites to make way for farmland, schools, cemeteries, and housing, shown 
in Figures 8.7a and 8.7b (Parcak 2007b). If the rates of urban development continue in 
the Delta it is possible that all agricultural lands and associated archaeological sites are 
at risk of being lost in 70 years (Salem et al. 1995). In addition, Egypt is facing rising 
salinity levels in the soil, causing the encroachment and subsuming of archaeological 
sites for cultivation (Goossens and Van Ranst 1998). Development is the single largest 
threat to the discovery of previously unknown archaeological sites in Egypt, which num- 
ber in the many thousands. If archaeologists have excavated less than one-hundredth 
of 1 percent of all known archaeological sites in the Egyptian Delta, the amount of 
information to be gained from archaeological site preservation initiatives is staggering 
(Parcak 2008). 

Urbanization is a factor that governmental policies may be able to control, unlike war, 
which allows looting and archaeological site damage to occur at unprecedented rates. 
It is difficult for archaeologists to assess the exact amount of damage war has caused to 
archaeological sites, as higher resolution satellite imagery is available only from 2002 
onwards, and aerial photography may not exist or be possible to obtain. Coverage of 
the area in question may not exist at all, or a comparative analysis may not be possible 
with only one higher resolution image available. Cultural heritage preservation is not 
generally part of an invading army's agenda. Destroying the history and culture of an 



.7 Ismu al-Arus, Middle Egypt, note: (a) cropmarks in photograph compared to 
(b) Quickbird image (scale 1:90 m), arrow points to the areas seen in the photo- 
graph, photograph by Sarah Parcak and 2008 Quickbird image courtesy of Google 



occupied country only aids demoralization and asserts the so-called "superiority" of the 
invading group, as evidenced by the destruction of the Bamiyan Buddahs by the Taliban. 
Environmental byproducts of war, such as the oil soaked deserts sands of Kuwait from 
the first gulf war, can be detected from space (Anon. 1992). These byproducts of war aid 
in the obscuring of ancient landscapes. The embargo and subsequent inflation in Iraq 
led to additional lands under cultivation plus looting, with over 8500 objects missing 
from regional museums and bomb craters at known sites (Nashef 1990, 1992). 

Ongoing looting of archaeological sites has turned large archaeological mounds in Iraq 
into the equivalent of waffles, with looting pits covering the entirety of sites. At present, 
the main group guarding archaeological sites in Iraq is the Italian Carabinieri, but at what 
point does the potential loss of human lives outweigh the protection of cultural heritage? 
After drug and gun smuggling, the looting of archaeological sites is the third-largest 
factor funding the insurgency in Iraq (Bogdanos 2005), so protecting archaeological 
sites is critical to the ongoing efforts there. Can remote sensing aid in preventing the 
looting (Figure 8.8) and smuggling in Iraq, Afghanistan, and other locations in the 
world? Satellite imagery analysis can, at best, monitor and detect likely border crossings 
where smuggled objects are likely to appear, aiding in mapping key places, like the Iraq 
National Museum, to be protected prior to an invasion. Protecting cultural heritage is 
now a Department of Defense (DoD) priority, with the DoD employing archaeologists 
(Anon. 2004) to discuss prevention and preservation strategies. Until heritage is a global 
priority, and the international illegal antiquities markets are shut down in Switzerland, 
Japan, and in well-known international auction houses, the destruction of archaeological 
and cultural remains will remain an unfortunate byproduct of war. 

Jokha, Iraq (scale 1:118 m), note the looting pits, 2008 image courtesy of Google 
Earth™ Pro. 



Protecting heritage using satellite remote sensing prompts more questions than it 
provides answers. If archaeologists monitor thousands of known archaeological sites 
with remote sensing, whose responsibility does it become to protect these sites? Is 
it the responsibility of the country where the site is located, where funding may be 
an issue, or is it responsibility of the group monitoring the site? What about any 
previously unknown archaeological sites detected from space? Some archaeologists assert 
that a facility to handle all world heritage sites is everyone's responsibility, and that a 
global GIS database should be created (Holcomb 2002). Every country in the world has 
different heritage management policies (if such exist), with international organizations 
with their own specific projects and priorities. Ultimately, protecting archaeological 
heritage occurs at a local level, whether through international organizations based locally, 
or through individual government employees (Hadjimitsis et al. 2006). Having concern 
for archaeological heritage occurs at a global level, and is a global responsibility to the 
extent that it is international organizations that can provide resources and training to 
local people to promote conservation. 

An international multilateral heritage agreement, the equivalent to the Kyoto Accord 
(Peter 2002), would aid in setting global priorities, but it is not probable that heritage 
could attract as much attention on a global scale as global warming. Each country 
has specific planning permission guidelines for development, but the European Union 
guidelines merit discussions. The EU Planning Policy Guidance 10, set in 1990, requires 
evaluation for archaeological remains from aerial photography at a 1:2500 scale, and 
ground-based investigation. The cost of the analysis is born by the developers (Palmer 
2002a, 2002b). Should other countries or regions adopt this same aerial approach? It 
would appear to be time- and cost-efficient, as using higher resolution satellite imagery 
or aerial photography from Google Earth™ would require minimal training. Individual 
country or region priorities remain at the heart of the matter. 

Remote sensing is playing an increasing role in heritage initiatives at international 
levels. UNESCO promotes remote sensing to protect both cultural and natural resources, 
and has numerous partner organizations. In order for an archaeological site to be nom- 
inated as a world heritage site, a lengthy process is undergone whereby a detailed site 
description plan is submitted to UNESCO. In addition to information about the archae- 
ological site, a detailed site management plan is required by UNESCO, which describes 
how a site will be protected. This requires maps showing areas of the site most threatened 
by natural or anthropomorphic features. In 2000, the site of Merv, seen in Figure 8.9, a 
world heritage site in Turkmenistan, was named as one of the world's 100 most threat- 
ened archaeological sites. Archaeologists used IKONOS imagery to create a base map 
for future conservation efforts (Ziebart et al. 2002). Satellite imagery needs to be used 
within a GIS framework in order to create a buffer zone surrounding the archaeological 
site. Land use and land cover change analysis must be created, in order to create predic- 
tive models for the landscape surrounding the nominated archaeological site (De Maeyer 
et al. 2002), so they can be protected and preserved. 

Education and outreach will aid in the more widespread usage of remote sensing 
in archaeology for heritage and conservation. There is increased interest from the 
general public for remote sensing in archaeology, especially with the advent of Google 
Earth™. How archaeologists should design remote sensing courses for their own stu- 
dents, colleagues, the general public, and international archaeology bodies, will differ 
according to the need and level of computing backgrounds. Funding from US AID 



Figure 8.9 Merv, Turkmenistan, broader landscape surrounding site showing intensified agricul- 
ture, 2008 image courtesy of Google Earth™ Pro. 

allowed Elizabeth Stone to set up a remote sensing training school for Iraqi archae- 
ologists at Stonybrook University (Stone 2008), but this is not possible in every 
situation with more limited funding. Distance learning for satellite technology in 
archaeology (Birnbacher and Koudelka 2002) is not feasible. Person to person contact 
is critical for teaching satellite remote sensing in a classroom, while technical train- 
ing requires long-term commitments to both the classroom and the remote sensing 
laboratory. More generalized outreach education should not be difficult, while accom- 
panying ethics training for students in archaeological remote sensing classes should 
be mandatory. 

One wonders if growing up with Google Earth™ in the classroom will change the 
perspectives of future generations of archaeologists. The author grew up anticipating 
National Geographic each month, and first "visited" many of the world's archaeological 
sites through the photos therein. Children can visit archaeological sites with connected 
photos on Google Earth™ any time of the day, and pursue websites for more detailed 
information. UNESCO is promoting outreach activities, via their program for Global 
Learning and Observations to Benefit Environment, for secondary school teachers and 
scientists (Ishwaran and Stone 2002). Will the wonder of archaeological discovery be 
dampened for future generations? The technology of remote sensing is advancing too 
quickly for this to occur. Students enter the field of archaeology with a better under- 
standing of how they can apply remote sensing to their research, which will aid in 
applying future versions of remote sensing technology. 

International initiatives to promote teaching remote sensing aid in promoting archae- 
ological site conservation. At the central campus of the International Space University in 



3.10 Supreme Council for Egyptian Antiquities, Cairo, during 2004 training, photograph 
by Sarah Parcak. 

Strasbourg, a two-month summer session teaches satellite remote sensing to international 
graduate and undergraduate students of archaeology (Farrow et al. 2002). During the 
2007 session, students created a manual for remote sensing usage in archaeology as a 
summer project with an accompanying field school (pers. comm., Williamson 2007). 
A similar field school program exists in Italy, based at Rome University "La Sapienza." 
Students receive training in basic archaeological method, landscape theory, GIS, applied 
satellite remote sensing, landscape modeling, and GIS database creation, with the courses 
taught by an international team of archaeologists. Survey forms an integral part of many 
international archaeological field schools, but remote sensing is not yet taught as part of 
survey training. International field schools should be designed to reflect the needs of each 
targeted group or organization. The author taught an intensive three-day remote sens- 
ing course, seen in Figure 8.10, to members of Egypt's Supreme Council for Egyptian 
Antiquities (SCA), in the office of the Egyptian Antiquities Information Service (EAIS) 
(funded through the US State Department Ambassador's Fund for Cultural Preservation), 
focusing on the major archaeological conservation and preservation issues for Egyptian 
archaeology. The author noted an increased usage of satellite remote sensing by the EAIS 
and SCA following the course. 


Ethical usage of remote sensing for archaeology is a topic that has not undergone signif- 
icant discussion amongst scholars. How to approach the issue of ethics in archaeological 
remote sensing is not clear, due to the overlap between the statements of ethical usage 



of remote sensing and ethical archaeological practices. How can archaeologists using 
remote sensing walk the line between ethical archaeological practice and ethical remote 
sensing practice, while protecting sites against looting and preserving them against 
modern development? It is necessary to review and discuss both types of ethics state- 
ments, examining all of the ethical issues relating to remote sensing usage in archaeology. 
This promotes the creation of a specific archaeological remote sensing ethics statement, 
which will encourage good remote sensing practice while discouraging data sharing that 
can lead to looting. It is up to each individual archaeological remote sensing specialist 
to put these statements in practice. 

The first ethics statement to be examined is the "Code of Ethics of the American Society 
for Photogrammmetry and Remote Sensing," approved in 1975. This is a major body of 
remote sensing specialists. The statement has existed for nearly 35 years (nearly as long 
as multispectral remote sensing), and remains fully relevant today. The statement can 
be found on the American Society for Photogrammmetry and Remote Sensing website, 
ht tp : //www. asprs . org : 

Honesty, justice and courtesy form a moral philosophy which, associated with 
mutual interest among people, should be the principles on which ethics are 

Each person who is engaged in the use, development, and improvement 
of the mapping sciences (photogrammetry, remote sensing, GIS, and related 
disciplines) should accept these principles as a set of dynamic guides for conduct 
and a way of life rather than merely for passive observance: 

Code of Ethics 

Accordingly, each person. . .shall. . . 

1 . Be guided in all professional activities by the highest standards and be a 
faithful trustee or agent in all matters for each clients or employee. 

2. At all times function in such a manner as will bring credit and dignity to 
the mapping sciences profession 

3 . Not compete unfairly with anyone who is engaged in the mapping sciences 
profession by: 

a. Advertising in a self-laudatory manner; 

b. Monetarily exploiting one's own or another's employment position; 

c. Publicly criticizing another person's working in or having an interest 
in the mapping sciences; 

d. Exercising undue influence or pressure, or soliciting favors through 
offer monetary inducements 

4. Work to strengthen the profession of mapping sciences by: 

a. Personal effort directed toward improving personal skills and knowl- 

b. Interchanges of information and experience with other persons inter- 
ested in and using a mapping science, with other professions, and 
with students and the public; 



c. Seeking to provide opportunities for professional development and 
advancement of persons working under his or her supervision; 

d. Promoting the principle of appropriate compensation for work done 
by persons in their employment 

5 . Undertake only such assignments in the use of mapping sciences for which 
one is qualified by education, training and experience. . . 

6. Give appropriate credit to other persons and/or firms for their professional 

7. Recognize the proprietary, privacy, legal, and ethical interests and right of 
others. This not only refers to the adoption of these principles in the gen- 
eral conduct of business and professional activities, but also as they relate 
specifically to the appropriate and honest application of photogrammetry, 
remote sensing, geographic information systems, and related spatial tech- 
nologies. Subscribers to this code shall not condone, promote, advocate, 
or tolerate any organization's or individual's use of these technologies in a 
manner that knowingly contributes to: 

a. deception through data alternation; 

b. circumvention of the law; 

c. transgression of reasonable and legitimate expectation of privacy. 

This ethics statement recognizes the scientific nature of remote sensing. Inappro- 
priate data manipulation in remote sensing, like any scientific field, can occur easily, 
thus necessitating a discouragement of this practice. Data alteration is, however, diffi- 
cult to detect, especially without ground-truthing to confirm project results. Remote 
sensing is a highly interdisciplinary field, requiring multiple levels of human interac- 
tions to facilitate and implement discoveries or research. The issue of "undertak(ing) 
only such assignments in the use of mapping sciences for which one is qualified by 
education, training and experience," is a more difficult issue. Who decides who is 
a qualified remote sensing specialist? One can become a member of a learned inter- 
national society, or take training courses from remote sensing companies to gain a 
certificate, but determining who is and is not qualified to do remote sensing work is 
not a straightforward matter. One might assume that a PhD in some area of remote 
sensing might qualify an individual to do remote sensing work, but then what of 
MA students who specialize in geospatial studies, or undergraduates with geography 
degrees and several years of remote sensing to project experience? How do professors 
who learn about remote sensing later in their careers fit into the overall experience 
picture, or laypeople with backgrounds in science who take university or community 
college courses, not to mention professional certification courses? Google Earth™ adds 
another dimension: All Google Earth™ users take part in remote sensing, and many 
users have discovered new features on the Earth's surface. The process of peer- review 
aids in determining what should and should not be published, but who qualifies as an 
"expert" and what that level of expertise allows with regards to remote sensing research 
is not well understood. The question of the "appropriate and honest application of 
photogrammetry, remote sensing, geographic information systems, and related spatial 
technologies" is another issue that requires discussion as it applies to archaeological 



Ethical applications of general remote sensing belong more in a legal realm, yet 
international remote sensing law is still a developing field. For example, an Indian 
court was asked to ban Google Earth in the wake of the late 2008 terrorist attacks in 
Mumbai, with lawyers claiming that Google Earth was used for planning purposes by 
the terrorists (Anon. 2008). What about the dozens of companies in the USA and across 
the globe who offer slightly higher- resolution data at a premium cost? Anyone with 
money anywhere in the world can purchase high-resolution satellite imagery. Although 
companies request the purchaser describe how and why the satellite imagery will be 
used, it seems any "reasonable" description is accepted. Though this data is not free, it 
is publicly available. Each government restricts information in a different way, yet data 
is easy to share, and easy to import via a handheld PDA. For example, during fieldwork, 
a prototype mobile application could support transfer of updated maps and data back 
to the PDAs using compact flash (CF) cards. Broadband satellite access points allow for 
international data communication via the Internet to a university-hosted database and 
web map server application. There will only be additional international ramifications 
for the increasing spatial resolution of satellite imagery and data-sharing. 

Remote sensing laws in the USA address some of the complicated issues relating 
to ethics in remote sensing. The Land Remote Sensing Policy Act 1992 authorizes the 
Secretary of Commerce to issue licenses for the operation of private Earth remote sensing 
space systems, with the authority delegated to NOAA. It is illegal for an organization to 
launch a system without a license. An act entitled The National Defense Authorization 
Act 1997, restricts remote sensing data distribution for Israel, which is interesting given 
that that the author has not come across a single multispectral satellite archaeology case 
study from Israel. Regarding major remote sensing policies, there is an interagency 
memorandum of agreement between the Departments of Commerce, State, Interior and 
the CIA, which provides for shutter control on private remote sensing satellites and 
other technical issues. In 2003, the USA instituted a commercial remote sensing policy, 
whereby NOAA has to publish, develop and review remote sensing policies. It encour- 
ages the development of space systems in the USA, but an FCC license is needed for the 
satellite frequencies and to launch the satellites. Additional information about space and 
law can be found online at the University of Mississippi School of Law's National Center 
for Remote Sensing, Air, and Space law at: 

Legal ramifications of the usage of satellite imagery extend to archaeology. There 
are some contradictions between the ASPRS ethics statement and the ethics statement 
adopted by the Society for American Archaeology (Lynott and Wylie 1995). It is recog- 
nized that a number of international archaeological learned bodies have ethics statements. 
The SAA ethics statement covers a broad spectrum of issues, and is the ethics statement 
most cited by American archaeologists in the classroom or at conferences. Each state- 
ment will be cited and discussed with regards to how they can be connected to remote 
sensing practice for archaeologists. Six main principles found the SAA ethics state- 
ment: Stewardship; accountability; commercialization; public education and outreach; 
intellectual property; and public reporting and publication. 

The first principle, that of stewardship, states: 

1. It is the responsibility of all archaeologists to work for the long-term 
conservation and protection of the archaeological record by practicing and 
promoting stewardship of the archaeological record . . . they should use 



the specialized knowledge they gain to promote public understanding and 
support for its long-term preservation. 

Conservation and protection of the archaeological record are the primary concerns of 
archaeological remote sensers, and looting fits into both of these categories. Looting 
prevention is recognized as a significant issue across the globe, and looters will use any 
tools at their disposal to gain information about archaeological sites or to obtain knowl- 
edge about the locating of possible items they could sell. How remote sensing might aid 
in looting is a major topic for consideration, especially as satellite imagery improves in 
resolution and more becomes publicly available. Looters frequently follow archaeologi- 
cal expeditions, or pay family members to "spy" on activities. The author worked on a 
Roman-Crusader fortress along the Mediterranean, when the team uncovered a marble 
capitol in the late afternoon. Only the team had seen the capitol, and they reburied it for 
removal the following day. The capitol had disappeared by early the next morning, and 
it became clear that people would visit the site each evening and probe the trench to see 
if the team had uncovered anything of interest. Many archaeologists tell similar stories. 

Given all of the problems with archaeological looting, how is remote sensing con- 
tributing to the international illegal antiquities market? Remote sensing helps and 
hinders looters at the same time. Archaeologists can monitor sites using high resolu- 
tion imagery, but the cost of this activity becomes quickly prohibitive. Free programs 
such as Google Earth™, and World Wind make it fairly easy to zoom in on known 
archaeological sites free from modern vegetation. Individuals have the right to place a 
bookmark with uploaded photographs anywhere in the world, thus giving anyone the 
right to give coordinates and images of previously unknown archaeological sites and 
features. Someone living next to such an area might not think anything of it, or might 
somehow want to attract a network of looters to their town for personal gain. Looters 
can use these programs to plan routes to and from sites, and to pick the best places to 
dig for antiquities. 

Fortunately and unfortunately, free imagery programs online frequently allow the 
discernment of subsurface architecture. Looters can also choose the best places to hide 
their goods, plotting points to avoid such as police checkpoints. Remote sensing may 
become more automated in the future, making it possible for people with no remote 
sensing skills to locate previously unknown sites from their soil or vegetation signatures. 
Training local authorities on free imagery programs can help to prevent looting via 
detection of likely places where looters might strike, but computer access is not always 
guaranteed. Images on free viewing programs can be outdated. High-resolution images 
would be able to record robber holes between excavation seasons, but would likely do 
little to stop destruction of sites as looting often takes place in areas where archaeological 
site guards are poorly paid (if any exist in the first place). Many parts of the world do 
not yet have high-resolution data available on these programs, but they will within a 
few years, when even higher- resolution data may be visible. 

Stewardship extends to protecting sites for future generations, but what is the respon- 
sibility of the archaeologists towards the individuals living on or by archaeological sites? 
During surveys, people can be afraid to report any archaeological remains on their prop- 
erty for fear that their homes and land may be confiscated. Archaeologists specializing 
in remote sensing must think deeply about how their survey data may affect the lives of 
people on the ground. Illegal home construction and agricultural reclamation goes on 



Figure 8.11 Tell el-Amarna, Egypt (scale 1:2678 m), showing agricultural expansion, 2008 
image courtesy of Google Earth™ Pro. 

near or on archaeological sites around the world, yet what choices do people have when 
their families need to be fed and housed and corrupt officials allow this to occur? At Tell 
el-Amarna, a Pharaonic capitol city in Middle Egypt dating to ca. 1300 bc (Kemp and 
Garfi 1993), over 90 hectares of the city have been lost in the past 30 years due to agri- 
cultural expansion and urban development, detected through satellite imagery analysis 
(Parcak 2005). In 2006, officials came in and bulldozed a number of homes illegally 
constructed atop the city. There was no warning to the inhabitants of the illegal housing. 
If the agricultural expansion is not stopped, however, most of the city will be gone in 
25 years (Figure 8.11). Can local officials monitor things year by year, as another 30 or 
40 feet of desert are added to people's agricultural plots? This is not likely, as remote 
sensing has shown how much this is affecting one of Egypt's best-preserved cities. On 
the West Bank of Luxor, people had built dozens of homes atop elite tombs in the village 
of Qurna. Authorities bulldozed these homes in 2007, many of which were used to mask 
the theft of antiquities from the tombs below (observed by the author in 1999). 

Issues of protecting sites and looting are rooted in global issues like poverty, access to 
medical care, and food. In Egypt, international aid agencies often have little choice but 
to build hospitals, schools, or water treatment plants atop archaeological sites, as it is the 
only land free from cultivation or development. Do archaeologists have the right to stop 
this type of development when it enhances the lives of poor people? Over one-third of the 
archaeological site of Tell Tebilla, a Late Period (ca. 600 bc) site (Mumford 2003, 2004), 



was removed for the construction of a water plant by US AID. No information is available 
about the area of the site removed, and it took over 20 years for the water plant to become 
semi-operational. The people in the surrounding villages, however, now have access to 
clean water. More coordination is needed between these aid agencies and archaeological 
organizations in countries around the world. Every country will have their own policy 
regarding the survey of sites before potential removal, but when so much is at stake of 
being lost, more could be done using satellite imagery. Moral issues of global access to 
healthcare, education, food, and water are serious issues that, at present, clearly conflict 
with the idea of preserving heritage and protecting sites. 

The next category of the SAA's statement of ethics deals with the issue of 

2 . Accountability requires an acknowledgement of public accountability, so 
consult actively with the affected groups . . . 

These affected groups include the people or groups where the archaeological work is 
taking place as well as the archaeologists conducting the excavation or survey work. 
As satellites improve in their spectral and spatial resolutions, many issues concerning 
people's rights to privacy will become relevant. Additional issues involving ownership 
of data, in particular archaeological data, will need to be debated. At present, the author 
can publish on any remote sensing topic in archaeology. Although her colleagues might 
be somewhat confused to see her name on a remote sensing publication involving horse 
burial sites in the Russian Steppes, or Andean elite housing in Peru, as long as she 
had used sound remote sensing and archaeological method, the papers would likely be 
accepted via the process of peer- review. Any remote sensing specialist in archaeology has 
the right to publish following this process. The press, however, does not employ this 
method of critique. Thus, what will happen to the process of archaeological discovery 
when any individual can order high-resolution satellite imagery of ongoing excavations 
and publish the results prior to the end of an excavation season? Everything in archae- 
ology is naturally context based: People examining a feature from space will not be able 
to examine material culture or radiocarbon dates to assess the overall importance of an 
excavated feature. The general public, however, would comprehend the significance of 
uncovering of a new temple at a well-known site. 

The right to publish the data on newly discovered features would become a topic of 
debate closely connected to the idea of accountability. Would the individual or group 
who "discovered" the newly excavated features from space be credited with the discovery, 
or would the credit for discovery rightfully remain with the group of excavators on the 
ground? Publication of archaeological excavations can take years: Should an embargo be 
placed on the publications of all archaeological data that is not written by the individuals 
in charge of the excavation? This is not something that would be accepted by the larger 
archaeological community, as scholars frequently publish old excavation reports long 
after the initial excavators have died. While the process of peer review would prevent 
the publication of archaeological material by amateurs who "discovered" the features or 
sites in question, the press would not adhere to these standards. Credit for archaeological 
discovery does not always come from scholarly publications, as it is attributed generally 
to those who grab the headlines. This is a topic that requires serious consideration, and 
is one that could readily become out of hand. 



Figure 8.12 Mt. Ararat, Turkey (scale 1: 3456 m), 2008 image courtesy of Google Earth™ Pro. 

Already, educated laypeople are making spurious claims of archaeological discovery 
from space (Taylor 2007), such as the so-called "discovery" of Noah's Ark, to which the 
press and reputable publications gave serious consideration. According to the team in 
charge of this work, "ship shaped objects" appeared atop Mt. Ararat in Turkey (seen in 
Figure 8.12) on IKONOS imagery, which the team then compared to a copper plated 
illustration from the 1600s to prove the object was indeed Noah's Ark. This unfortunate 
usage of remote sensing for archaeological discovery had significant flaws. No proper 
remote sensing analysis or specialist can be associated with the "discovery" of this feature. 
Geologists have stated that the anomaly is a fairly common geological formation (a rock 
ledge covered in ice) thus exposing the find to be more in the realm of science fiction 
than science (Cline 2007). 

Once again, hype and the "story" triumphed over archaeological method. Both the 
press and archaeologists are accountable to the general public. Proper remote sensing, 
as shown by many hundreds of peer-reviewed publications cited in this book, requires 
rigorous scientific analysis followed by thorough ground-based testing, survey, and exca- 
vation. Similar stories to the one described will always gain excessive attention in the 
media due to their very nature: Combining space technology and the Bible, in addition to 
other legendary accounts, will undoubtedly yield future stories with little science to sup- 
port claims of an extraordinary nature. Only making the science of satellite archaeology 
more accessible to the general public can prevent pseudoarchaeology from triumphing 
over good practice. In addition to the process of peer- review, archaeologists should con- 
tribute stories to local newspapers, or publish newsletters. Good project websites receive 
a great deal of attention from school groups, while visits to local schools promote the 



practice of good science. Publishing accessible material promotes this practice as well 
as promoting archaeological accountability. 

The third area of discussion covers commercialization, an area of ongoing debate, 
especially as pertains to the publishing of items known to be looted. 

3 . Archaeologists should avoid, whenever possible, activities that enhance the 
commercial value of archaeological objects. 

Archaeological remote sensers not careful with their data dissemination can inadvertently 
support looting. Suspicious individuals frequently contact the author and her colleagues 
requesting detailed information regarding their survey work, so clearly looters have 
recognized the potential for using satellite imagery in their efforts. This problem extends 
to large conferences as well, with likely looters attending and posing as other scholars 
or students. Some scholars tell stories of being followed by individuals at conferences 
pressuring them for copies of maps and other information. Thus, people working in the 
field of archaeological remote sensing should not respond to anyone they feel is making 
unusual requests for data. 

What happens when archaeologists using remote sensing discover previously 
unknown archaeological sites where significant looting has taken place? Does this 
enhance the value of looted items with a known provenience? The majority of looted 
objects lose their provenience. People's financial gains from these previously unknown 
sites, however, cannot be understated, and are closely connected with larger global 
issues (including war). In a study examining drainage canals along the Holmul River 
in Guatemala using Landsat TM and ETM imagery, IKONOS, and STAR- 3 -DEM 
imagery, archaeologists noted that deforestation has contributed the illegal antiqui- 
ties trade. Looting is estimated to bring in US$10 million dollars a month (Sever and 
Irwin 2003). This is in a well-known area. How many ancient objects from Central 
America can be located on Ebay®, or in antique stores in Paris, Geneva, and London? 
Many objects are fakes, but a surprising number of real objects make it onto the public 
market. Looting is also connected to more "major" crimes, as seen by the situation in 

Elizabeth Stone's research using Quickbird satellite imagery found 16 km of loot- 
ing holes in 101 km of archaeological sites located in southern Iraq, which was the 
equivalent to four times the total excavated area in the past 100 years. She noted that 
looting took place in the mid-1990s during a time of extreme poverty, and in 2003 
during the insurgency (Stone 2008). Only a concerted international effort between 
government agencies, archaeologists, and the general public will stop this and related 
looting, which makes organizations such as Saving Antiquities For Everyone (SAFE, see all the more valuable to archaeology. 

Public education and outreach, the fourth category, are the main ways that archae- 
ological sites can be protected against looting and conserved, as well as getting public 
support for the stewardship of archaeological record. 

4. Archaeologists should reach out to, and participate in cooperative efforts 
with others interested in the archaeological record with the aim of improv- 
ing the preservation, protection, and interpretation of the record. In 
particular, archaeologists should undertake to: (1) enlist public support 



for the stewardship of the archaeological record; (2) explain and promote 
the use of archaeological methods and techniques in understanding human 
behavior and culture; and (3) communicate archaeological interpretations 
of the past . . . 

Explaining and promoting the use of archaeological methods and techniques in under- 
standing human behavior and culture is the job of all archaeologists. Remote sensing 
and its usage also help to communicate archaeological interpretations of the past. How 
each individual will use this information is not something any archaeologist can predict. 
Educating the public on the ethics of archaeology would be just as important as educat- 
ing them on how archaeologists assists with interpreting the past. Unfortunately, not 
all individuals can be trusted. 

An ongoing recent case involves a US army warrant officer based in Heliopolis in Cairo. 
Egyptian authorities recorded 370 objects as missing from the Maa'di museum. The offi- 
cer sold 90 objects to a Texas-based art dealer for $2 1 ,000, claiming the objects passed to 
him from his grandfather, who had worked in Egypt in the 1930s and 1940s. The dealer 
then supposedly consigned these pieces to collectors and galleries in Manhattan, London, 
Zurich and Montreal (El-Aref 2008). While the case is still ongoing, it underscores the 
fact that the temptation to steal or loot can affect those often charged with protecting a 
country's cultures and heritage. These can include archaeological site guards, who make 
very little money, or site workers themselves, as well as individuals living on top or 
next to archaeological sites. Educating the individuals who would have easiest access to 
material from archaeological sites would aid in stopping looting. This starts at the level 
of local agencies that deal with water or land management. 

The fifth and sixth topics, those of intellectual property and public reporting and 
publication, are very closely related areas. The first, intellectual property, is a topic 
which applies more readily to archaeological excavations than archaeological remote 

5. If there is a compelling reason, and no legal restrictions or strong coun- 
tervailing interests, a researcher may have primary access to original 
materials and documents for a limited and reasonable time, after which 
there materials must be made available to others. 

Archaeological inventories are the first area that may cause problems. In the USA, a 
national inventory of archaeological sites was not yet complete in 1987 (Williamson 
1987), and is not something that is publicly accessible today. Technology transfer is 
an important issue in preservation management, yet do countries have the computer 
equipment and related facilities to store this data (Campana and Frezza 2006), and 
if they do, how well-guarded would it be from theft by looters? The best place for 
looters to work is within the government system. The USA, supposedly one of the more 
data-protection conscious nations in the world, frequently has data-theft issues, seen 
frequently on the news. Information does not appear to be safe in any format. Working 
closely with foreign archaeological bodies to ensure data protection may help. Looting 
may not threaten archaeological sites as much as ongoing development, so sharing 
information and maps is necessary to help government agencies with site management. 
Ultimately, no databases are secure. 



Data sharing and data access are additional ethical issues within archaeological remote 
sensing which tie into the sixth category of the SAA ethics statement: 

6. An interest in preserving and protecting in-situ archaeological sites must 
be taken into account when publishing and distributing information about 
their nature and location. 

Many satellite datasets are freely available over the Internet, while higher resolution 
satellite imagery datasets have additional costs associated with them if they are to be 
used by more than one individual. Is it ethical for archaeologists to share higher resolution 
data with international colleagues (who could not afford such imagery datasets) without 
paying higher costs to commercial companies? At present, such agreements are based 
on an honor system, yet data-sharing is a common practice. 

Overall access to archaeological site data is an additional issue for debate, especially as 
many funding agencies demand public access to any data generated from projects through 
websites or published data. This remains a necessity, but the information should be made 
available in such a way that it protects archaeological sites. Non-specific site location data 
should be available on the Internet or on specific site pages. Most archaeological journals 
are available only in research libraries or centers on campuses, where the individuals who 
have access are limited. JSTOR and other online reference journal databases tend to be 
available only on campus networks, with a three to four-year gap in the most recent 
publications featured. Ultimately, archaeologists must consider the most important 
factors in determining what information should be made available. If looters want access 
to archaeological site data and publications, and have an extensive (and well-funded) 
international network, it is likely that they will be able to get some of the information 
they require. 

Following data-protection models from other fields will help to protect archaeological 
sites. In the fields of Public Health and Medicine, the US Government requires that 
patient data is geocoded to protect the rights of each individual patient. GIS plays an 
important role in the geocoding that makes this possible. For example, individual data 
points are given numbers/codes, and the data is further hidden through generalizations 
made about broader areas (city blocks, or 1 km zones). The same information should be 
applied to archaeological sites found through remote sensing analysis. No specific GPS 
coordinates should be given for any data found through satellite remote sensing analysis 
and subsequent survey. The data can be masked through publishing more generalized 
maps that contain points, perhaps with some type of error or offset introduced to confuse 
potential looters. Actual site data points might be accessible via a secure online network, 
or in person-to-person contact. 

Satellite imagery will appear more over time in court cases dealing with property 
disputes or other illegal activities, and it remains a question if archaeological projects 
that publish exact coordinates of archaeological sites of features that are later looted or 
defaced should be held liable. It is the responsibility of each archaeological project to 
protect, to the best of their ability, the places where they excavate or survey. Previous 
knowledge that looters will use any and all information to loot or harm sites should 
be a key consideration. This is why a specific archaeological remote sensing code of 
ethics should be developed, to promote data sharing and publishing of archaeological 



remote sensing data in a responsible way. Should international organizations that harm 
archaeological sites, either advertently or inadvertently, be held responsible as well? At 
this stage, it is too early to answer this question. 

This is closely connected to international wars or internal country conflicts. The US 
Department of Defense now has heritage as a priority, educating servicemen and ser- 
vicewomen to be sent abroad in the heritage of both Afghanistan and Iraq. Funding 
for these activities is aimed mainly at the Middle East: What about other threatened 
parts of the world? Archaeologists may wrestle with the idea of helping the Depart- 
ment of Defense with their efforts abroad, citing either political or moral issues. If US 
government agencies express an interest in preserving the heritage of foreign countries, 
however, it is not an opportunity to be missed. The DoD has a number of archaeologists 
and museum curators supporting their efforts, showcased at several major archaeology 
conferences including the Archaeological Institute of America and the World Archae- 
ology Conference. It is hoped that this will support future heritage and conservation 
efforts abroad. One wonders what is happening to the cultural heritage of countries 
under severe internal pressures such as Zimbabwe, shown in Figure 8.13, Burma, or 
North Korea. 

Only through the creation of an independent set of ethics guidelines for archaeological 
remote sensing can the conflicts between the ASPRS and SAA statements of ethics be 
resolved as well as emphasizing the positive similarities. This ethics statement draws on 
both sets of guidelines. It is hoped that archaeologists practicing remote sensing will 

Figure 8.13 Great Zimbabwe, Zimbabwe (scale 1:113 m), circular structures are in the central 
part of the image and are partially obscured by vegetation, 2008 image courtesy of 
Google Earth™ Pro. 



adhere to a similar ethics statement. There is no international body (as of yet) to promote 
the usage of or adherence to these guidelines. 

1. Archaeological remote sensers shall not publish data that could potentially harm 
archaeological sites, features, related material culture, or the people surrounding 
the archaeological sites and features. 

2. Archaeological remote sensers should, to the best of their abilities, promote good 
remote sensing practice in the field, classroom and other applicable situations 

3. Archaeologists will not create false satellite remote sensing data to their personal 
benefit (either for monetary gain or for press coverage). 

4. When publishing their data or putting data on the Internet, archaeologists using 
remote sensing will geocode data points for unknown or little-known archaeological 
sites and features, or they will introduce error into their maps to discourage looting 
and to protect sites. 

5 . When publishing their data, archaeologists will discuss techniques that do and do 
not work, and will strive to disseminate their results as broadly and as responsibly 
as possible. 

6. Archaeological remote sensers should promote the usage of remote sensing in foreign 
countries through training, outreach, and data sharing. 

7. During ground- truthing, archaeologists testing satellite imagery during ground- 
truthing shall not undertake activities that risk the health and safety of their team 
members or affect local groups of people. 

The question of who will enforce these proposed ethical guidelines remains a question. 
Conference organizers, granting agencies, and article reviewers can all support them, 
but every group of individuals will have a vested interest in a particular approach. 
How the proposed ethical guidelines should be debated is another issue, and there 
may be additional topics that will be included in future as landscape issues evolve 
with globalization. There is still much to discuss on the topic of archaeological remote 
sensing and how it is connected to conservation, preservation, and ethics. Using a wide 
range of satellite image types discussed in this volume will allow the monitoring and 
protecting of known and unknown past sites and features through defining their specific 
spectral profiles. Future developments with higher resolution data and real time data 
will introduce additional topics for debate and discussion. While education and outreach 
remain the best ways to share satellite archaeology data, they need to be done in such a 
way that archaeological sites are not harmed. The ultimate goal of archaeology, after all, 
is not the discovery of past archaeological remains: It is to protect and preserve them for 
future generations. 



The previous chapters have illustrated the present state of affairs in archaeological 
remote sensing. It remains for me to introduce, however, the potential new directions 
that satellite remote sensing is taking, and additional ways satellite remote sensing 
might contribute to the broader field of anthropology. Remote sensing programs will 
undoubtedly improve over time, and more specific programs for archaeological work 
may be developed. More advanced computer programming is needed to improve spe- 
cific algorithms, but we are a long way from obtaining, if ever, any "automated" satellite 
archaeology: To realize this objective, innumerable sets of conditions and different signa- 
tures would need to be identified and put into remote sensing programs — not impossible, 
but quite far reaching. Each region of the world requires specific approaches that change 
according to weather patterns and each satellite image. If satellite imagery analysis can- 
not be made "automatic" in the foreseeable future, will ongoing computer programs 
require less training, or will specific programs for archaeologists be developed? Imagery 
programs may become more user friendly, but, like all advanced computer programming, 
will require coursework and training for interpretation. Remote sensing programs are 
not restricted to any one field, as the same techniques that can locate past archaeological 
sites can be used in every remote sensing subfield, including forestry, geology, physics, 
biology, and many others. 

The field of satellite archaeology will almost certainly offer more information to 
archaeologists, through additional resources, free satellite imagery, blogs, discussion 
boards, and, as time passes, additional written and electronic resources. As more discov- 
eries are made, increasing information will appear on the Internet, with project websites 
discussing surveying strategies. Archaeologists will need to rethink their surveying and 
excavation strategies in light of this new information, especially as sub-meter satellite 
images become commercially available. It seems likely that satellite archaeology will 
no longer remain in the domain of "technical" researchers: Archaeological applications 
are becoming just as broad and important as the techniques themselves. Hence, satellite 
archaeology, as a field, is clearly in its infancy. 

Archaeologists, then, are left to ponder what the future might hold for satellite 
archaeology, and how it will affect excavation and survey work. Graduate students 
across the USA and elsewhere are now emerging with increasing levels of technical 
training and experience that will affect how they conduct their own research projects. 
Many archaeological and anthropological positions now require a background in GIS 
and remote sensing: Increasingly universities in the USA are hiring recent doctoral 



graduates with such backgrounds. These skills are becoming an indispensable tool to 
many graduate and undergraduate curriculums across the globe, thus perpetuating the 
cycle of training in remote sensing (many archaeology classes now include GIS and 
remote sensing). 

Satellite archaeology has already earned its place as a sub-specialization of archaeol- 
ogy. However, there appear to be few courses at present with a sole focus on satellite 
archaeology, which normally falls under the broader study of "landscape archaeology." 
In future, course programs will need to incorporate remote sensing, GIS, geophysical 
science, and landscape archaeology, advocating a broader approach to the analysis of past 
landscapes. Mastering these approaches is necessary to recover as complete a picture as 
possible of past cultures and their environmental settings. However, more collabora- 
tion is needed between natural and social sciences: Archaeology is generally considered 
a social science, with some aspects falling in the natural sciences (e.g. DNA testing, 
radiocarbon dating, etc.), and remote sensing falls more strongly within the natural or 
applied sciences (e.g. physics, computer science, etc.). Applied satellite archaeology is 
more of a natural science, while the broader implications of satellite archaeology place it 
within the social sciences. Both fields play equally strong roles in satellite archaeology, 
which is why students of archaeology should be encouraged to take courses in physics, 
computer science, geology, and geography. Those courses will only enhance the ability 
of the next generation of satellite archaeologists. 

Broadening traditional satellite archaeology, namely research dealing with more 
historical periods, will facilitate its application in other areas of archaeology and anthro- 
pology (Couzy 1985; Kruckman 1987; Davenport 2001). For example, inOlduvai Gorge 
(Figure 9-1), archaeologists used GPS points to map layers of ash sediment, using geo- 
logical strata to trace associated hominid remains deposited throughout the landscape 
(Ebert and Blumenschine 1999). If there are consistencies in hominid deposition layers, 
then satellites should be able to detect similar spectral signatures elsewhere in land- 
scapes revealing additional hominid deposition layers. Digital elevation models would 
help physical anthropologists better map such landscapes. Satellite imagery can also 
detect past water sources, which in turn could yield evidence for adjacent early human 
activity. Similar approaches could be attempted for paleontology, where dinosaur bones 
appear in specific geological strata (Figure 9-2). 

Satellite imagery analysis has the potential to assist with larger anthropological and 
archaeological questions relating to past migrations through Africa, Europe, and the 
Americas by applying landscape reconstruction. Can past human behavior truly be 
mapped with satellites? So many anthropological questions relate to how and why 
human beings chose to live in a particular location. Satellite image data, especially 
those dealing with land use and land cover change, can facilitate ethnoarchaeological 
approaches to past landscape use with regards to crop patterns, field organization, and 
the use of local water sources for irrigation (e.g. the potential for past Mayan land use 
practices to be applied today). 

How human beings interact with their landscapes is a crucial question for anthro- 
pologists and archaeologists, and satellites offer a slightly different perspective on 
anthropomorphic landscape relationships. An ethnoarchaeological study of people liv- 
ing in a rainforest can assess how deforestation or climate changes may have affected 
their relationships with the land and how they adapted to changing climate conditions. 
Such studies also have implications for past and future climate change adaptations. 





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Figure 9. 1 Olduvai Gorge, Tanzania (scale 1:175 m), 2008 image courtesy of Google Earth™ Pro. 


Figure 9-2 Kill Site, Montana (scale 1:301 m), 2008 image courtesy of Google Earth™ Pro. 



Evaluating animal habitats from space may be useful for subsistence pattern studies, 
and certainly ties into broader strategies for cultural and natural resource management. 

One of the areas that will revolutionize the field of satellite archaeology is the improve- 
ment of spatial and spectral resolutions. By early 2009, 30 cm pixel (one foot) resolution 
satellite imagery will be available commercially via the same company now selling 
IKONOS imagery. This doubles the present Quickbird imagery resolution, while Digital 
Globe is planning for a 30 cm resolution satellite to be launched in 2009- This essen- 
tially equals the highest resolution found in aerial photography. Satellites offering 30 cm 
resolution will make it far easier to detect buried walls and buildings, and refine our 
perception about how narrow bands of soil and vegetation may be affected by buried 
structures. Archaeologists will also be better able to use this new imagery in the mapping 
and planning of existing sites in a GIS. 

It will not be much longer before a 10 cm resolution (4 inch) satellite will be launched, 
perhaps by 201 1. Like all computer- related technologies, satellite technology is advanc- 
ing at ever- faster rates. There are not many archaeological features (excluding material 
culture remains) smaller than 10 cm, while aircraft born LIDAR has a resolution of 3 cm. 
New strategies for analyzing higher- resolution archaeological data will emerge over time. 
Archaeologists can only speculate, at this moment in time, how good satellite data will 
become in 20 or 30 years. Perhaps we will be able to zoom in from space on individual 
potsherds and other small material culture remains, analyzing them in situ, making 
archaeologists completely rethink how surveys are conducted. Ground-based remote 
sensing will continue to improve as well, giving archaeologists ever-clearer images of 
buried architecture. Satellite and ground-based remote sensing may allow full 3D recon- 
structions of above- and below-surface architecture and features, enabling archaeologists 
to maximize their excavation efforts in the most significant areas. Then, and only then, 
will the gap between "dirt archaeologists" and remote sensers be bridged (Johnson 
1996, 2005). Archaeologists will also be able to request high-resolution imagery of 
their own archaeological sites at the end of an excavation season, getting an aerial 
view of a site when kite or balloon aerial photography might otherwise be restricted 
or unavailable. 

The resolution of SAR imagery is improving along with multispectral satellite 
imagery. Currently, archaeologists can purchase 2 m resolution SAR imagery, although 
the imagery is expensive, and has not yet been applied to archaeological exploration. 
Future SAR imagery will allow archaeologists to visualize buried architecture for the 
first time, as current SAR data is too coarse to define most architectural features. Smaller 
canals, channels, or even field boundaries may be detectable with higher resolution SAR 
data, which will certainly affect future work in satellite archaeology. Additional features, 
which currently lie too deeply buried beneath modern debris, towns, or in floodplains, 
might be discernable by future SAR images. 

Spectral resolution is another area that will only improve over time. Hyperspectral 
datasets, such as those offered by ASTER, are finally being applied to more satellite 
archaeology studies, with other hyperspectral data (some of which requires a sensor to 
be installed in a plane) also allowing more versatile analysis. Future hyperspectral satel- 
lites will offer far better spatial resolution. There is not, at present, a spectral database of 
archaeological features for each region of the world. Although spectral databases do exist 
for specific geological formations or chemicals, ancient structures may often utilize differ- 
ent types of stones and other materials during construction or subsequent modifications. 



Figure 9.3 Moundville, Alabama (scale 1:251 m), 2008 image courtesy of Google Earth™ Pro. 

On archaeological sites, the presence of stone buildings among less substantial structures 
and materials signify a highly stratified society, or suggest a general purpose and function 
for the buildings. Archaeologists can target even more specific bands for archaeologi- 
cal analysis, since the bands that detect ancient canals in Thailand will be different 
from the bands that detect ancient Mississippi culture mounds in the southern USA 
(Figure 9-3). Archaeological spectral databases will allow archaeologists to determine 
that not only that they have found a feature, but may further define what exactly the 
feature is. This will be extra beneficial to regions where ground-truthing is impos- 
sible, or, in the case of turbulent areas, may help in monitoring sites under threat 
of looting. 

Will the refinement and identification of spectral signatures advance to the point 
where their reflectance of an archaeological site, and areas within a site, determine a 
rough date for the site? This would appear unlikely given how much mixed debris 
often cover site surfaces, or how frequently ancient buildings have been modified in 
later phases of the archaeological record. However, if the satellite imagery can distin- 
guish multiple signatures for debris specific to certain periods of time (e.g. including 
volcanic ash layers from Pompeii seen in (Figure 9-4)), multiple site dates might be 
obtainable. In later periods where more specialized technologies are used, a combina- 
tion of certain materials might make dating a site from space possible. For example, 
Roman period lime kilns are distinct, and used large amounts of limestone (e.g. Mendes, 
East Delta, Egypt). Subsurface signatures indicating concentrations of limestone sur- 
rounding burnt-brick would likely prove to be Roman in date in areas where such 
technology normally reflects Roman activity. By isolating specific activities and their 



Figure 9.4 Pompeii, Italy (scale 1:234 m), 2008 image courtesy of Google Earth™ Pro. 

material expression that characterizes certain periods, one might be able to detect both 
cultural and temporal signatures through remote sensing. 

How quickly archaeologists are able to obtain high-resolution remote sensing data is 
another factor that should only improve over time. At the moment, much free coarser 
resolution data can be downloaded at will from websites, while high-resolution data 
can require payment and a waiting time of weeks to several months for tasking or 
delivery. Even companies offering a 24-hour "rush" data option prioritize their services 
to government agencies or companies paying for large data orders. In contrast, ordering 
imagery from data libraries is a relatively quick process, with delivery taking only 
several days. Such services will only improve and companies will only develop online 
databases for customers to purchase and download satellite data. A subscription charge 
may accompany this, depending on the overall quality of the imagery being ordered. 

Until much higher spatial and spectral resolution satellite data becomes available, it 
will take a long time for the current high-resolution satellite data to become much less 
expensive in what has become a very lucrative industry. For instance, the present "cutting 
edge" satellite imagery, such as Quickbird and IKONOS, will eventually become much 
cheaper. Today, to purchase the minimum sized land area (2 5 km ) for archived Quick- 
bird imagery costs US $ 10.00 per km , which is well within the affordable range for most 
archaeological excavations and graduate students. For infrared Quickbird imagery the 
cost rises to US$18.00 per km , thus amounting to US$450.00 for minimum area cov- 
erage, also well within most modest archaeological budgets. Once again, these costs will 
undoubtedly decrease once commercially available resolutions improve and additional 
discounts are offered to academic researchers. Regulations on data sharing may also 
change: At present, researchers are required to inform Digital Globe of whether other 



individuals will have access to their satellite data. The cost increases for each additional 
user. Although archaeologists can share high-resolution data, they are ethically bound 
to maintain their initial contract with Digital Globe. As in the computer industry, these 
image sharing rules and regulations are not likely to change drastically, since compa- 
nies are project-driven and rely on payments for high-resolution data, regardless of the 
individual purchaser's intent, be it educational or commercial. 

Is satellite technology advancing faster than archaeologists' ability to learn, apply, 
and analyze the data and programs, and all the inherent implications? Each time new 
satellite imagery programs appear on the market, there can be a delay before an archae- 
ological publication emerges discussing the application of the new satellite data. This is 
more a function of archaeological practice than the ability of archaeologists to recognize 
and apply fully new technology. Archaeologists usually must obtain grants to purchase 
costly new satellite imagery, and once they have analyzed the satellite imagery, need to 
ground-truth, or field test, their results. Once an article or monograph is submitted, it 
may take as long as one year before the data is published. The entire process can take 
two to three years before an article appears discussing new applications of imagery. In 
the meantime, archaeological teams may very well have adopted another satellite image 
or a more advanced remote sensing technique in conjunction with ground-truthing. It 
is therefore recommended that archaeologists start an online journal for archaeological 
remote sensing. This will decrease the lag time between paper submission and publica- 
tion, and will allow the publication of higher resolution color photographs that do not 
generally appear in conventional archaeological journals. 

Using aircraft, such as ultralights, has tremendous potential for the field of remote 
sensing in archaeology. Ultralights are sufficiently inexpensive (through rental), have 
enough carrying capacity to support LIDAR scanners, and often require less compli- 
cated permits than aircraft. Many ultralights operate around tourist areas, and are not 
seen as much of a security risk. Archaeologists have the advantage of contracting for 
ultralights more spontaneously than aircraft (pending budget restrictions), and can test 
flying missions closer to the ground. LIDAR can be combined with lower flying aerial 
photography at the end of excavation seasons, or survey seasons, where the team can 
direct the ultralight to exact locations of previously unknown archaeological sites. Not 
only can the team receive the modeling data from the satellite imagery analysis, but 
they can integrate additional levels of data for a more complete landscape model. 

Archaeological looting is one of the most important areas issues that satellite 
archaeology can help address. Real-time satellite monitoring may not be possible right 
now, but may be in another few decades. International cultural authorities may, some- 
day, be able to train satellites on culturally rich regions, hiring security specialists to 
monitor them remotely. Using high-resolution data, archaeologists or national antiq- 
uities organizations can currently monitor specific threatened archaeological sites on a 
monthly basis, but this is still too expensive and not effective enough; Many looted 
sites lie in inaccessible regions of the world (e.g. Iraq, as shown in Figure 9-5, and 
Afghanistan). Archaeological looting needs to be recognized as a more serious crime by 
governments, and international police forces (Interpol), who represent the only organi- 
zations sufficiently equipped to pursue and prosecute offenders at local to international 
levels. Another option may be to task satellites to spot check threatened sites regu- 
larly, and conduct specialist analyses via blogs. This may only aid looters, however, 
who have gotten smarter through time and adapt their looting strategies accordingly. 





Figure 9.5 Umma, Iraq (scale 1:144 m), note extensive looting pits, 2008 image courtesy of 
Google Earth™ Pro. 

Future archaeologists and heritage protection agencies will need to do the same thing 
to protect the past sufficiently. 

With regards to advancing satellite technologies, all remote sensing specialists need to 
refine constantly their methods of analysis, and need to continue to learn new programs 
throughout their careers. One of the best methods is through teaching: I continuously 
learn about remote sensing analysis from articles and commercial websites, and often 
speak to people in the satellite industry to learn about new directions. Even though 
I direct a remote sensing laboratory with health research as a major component (a 
focus of my university), this has only enhanced my ability to do archaeological remote 
sensing research. Remote sensing analytical techniques are similar no matter what the 
subdiscipline, and, at the end of the day, remote sensers model environments to fit 
their research questions. Both health and archaeology use remote sensing to visualize 
anthropomorphic and environmental patterns and how they change through time. By 
teaching students in diverse disciplines (public health, medicine, physics, biology, and 
many other areas), I am forced to think outside my archaeological box, to address diverse 
questions and research interests. Looking through lenses colored by different fields has 
actually helped focus my personal research questions, while broadening my remote 
sensing approaches. 

The field of satellite remote sensing, like archaeology, will always advance, becom- 
ing broader as satellite technology improves and the general public becomes more 
aware of its existence. The overall quality of science applied in archaeology improves 



annually with further education and training, but the applicability of applied satellite 
technologies for archaeology remains an issue. Although the techniques and methods 
of analysis will improve through time, the mentality of how archaeologists approach 
satellite archaeology should remain generally the same: Having an open mind and a 
desire to promote the best possible science and ethical practices. 

It is likely that press coverage remote sensing in archaeology will increase with addi- 
tional advances and associated discoveries. With recent features in National Geographic 
News (Handwerk 2006), Scientific American (Hvestendahl 2008), The Economist (Anon. 
2003), PBS-NOVA, the Discovery Channel, and foreign papers (Heimlich 2004), the 
public is becoming increasingly aware of how satellites can contribute to archaeological 
research. In more affluent nations around the globe, children are now being raised with 
satellite technology, while most computer literate people are aware of Google Earth™. 
Uploaded photographs of archaeological sites on Google Earth™ offer multiple perspec- 
tives to interested viewers. People no longer need to rely on picture-perfect photographs, 
such as those in publications like National Geographic, to gain a greater appreciation for 
past landscapes. 

Interpreting imagery for the purposes of past archaeological discovery is closely con- 
nected to how human beings can be influenced by the power behind imagery. Images 
can be controversial, powerful, and painful all at once (Steiner 1987). The responsi- 
bility of interpreting or indeed manipulating important imagery generally falls into a 
few hands, which are not always unbiased (Pursnell 1991: 918—20). Satellite imagery, 
with multispectral capabilities, has the ability to convey much more information than a 
typical photograph. Although a series of photographs over time can be effective, satel- 
lite imagery (which already has global coverage) can show broader changes over time 
for the past three to four decades, thus allowing each viewer to take an active role in 
determining the implications of these and future changes. A picture may be "worth a 
thousand words," but satellite imagery can be worth a million dollars: Politicians and 
international aid agencies have used sequential satellite images to raise millions of dol- 
lars in the aftermath of the December 2006 tsunami in southeast Asia, emphasizing the 
damage based on pre- and post-tsunami effects. During the August 2006 flooding in 
New Orleans, the sign "help" someone had painted atop a building could be seen from 
700 miles in up space, underscoring the individual human component in the disaster. 
Images of ancient cities and architectural features seen from space will certainly not 
inspire the same emotional response as images of global tragedies, but the power of 
wonder and curiosity should not be underestimated. 

Climate change will affect archaeological sites somewhat differently than urbanization 
and increasing populations, but is still an important factor to consider when studying 
archaeological landscapes. Regarding our current climate, change will probably be much 
greater in the lives of our grandchildren, depending on how quickly things advance over 
the next 30 years. The rate of population growth and changes in land use practices will 
affect future archaeological site destruction and preservation more than any other factor. 
It is essential to promote global education initiatives aimed at local and government 
tourism, heritage, and antiquities organizations, since only these institutions will be 
able to interfere effectively with the local populace to promote heritage preservation (if 
possible), which otherwise exceeds the mandate of foreign surveying projects. 

Satellite remote sensing projects carried out by archaeologists with backgrounds in 
both remote sensing and archaeology have much to offer, but satellite archaeology is 



not always a substitute for archaeological excavation or survey work: It is one of a 
number of tools that archaeologists can apply in their research. Archaeologists should 
be skeptical of any technology that appears to promise a solution to "all problems." 
Unfortunately, satellite archaeology is a tool that can easily be abused if one lacks 
sufficient training in either remote sensing or archaeology. Satellite remote sensing has 
its own inherent problems, many of which have been detailed above, that should be 
recognized before starting a satellite archaeology project. Everything will depend on the 
interests, background, and needs, of the archaeologist and their research team, and the 
restrictions of their specific research location. 

This treatment has aimed to make satellite remote sensing for archaeology more acces- 
sible and comprehensible for people with an interest in the subject, whether they are 
lay people, undergraduate students captivated by the possibilities of Google Earth™, 
graduate students who aspire to carry out their own survey projects, or professional 
archaeologists who wish to venture into satellite remote sensing for archaeology. This 
book has avoided any attempts to be an advanced technical manuscript. Should a reader 
be more interested in satellite remote sensing after perusing this book, they are rec- 
ommended to take a university course or examine any of a number of existing remote 
sensing manuals (referenced in the introduction). 

Satellite remote sensing for archaeology should not be viewed as an inaccessible 
science, nor should it be viewed as something that can be picked up "over the weekend." 
It requires dedicated study for those who wish to become proficient in its application, 
and, above all, an open and willing mind for the constant advances in both remote 
sensing technologies and archaeology. Our world and technology are changing far faster 
than we can observe and keep up with individually, and, with so many thousands of 
archaeological sites and features left to find and protect, satellite archaeology does not 
entail a race to see who can find the most archaeological sites, or a "lost" ancient city. 
Instead, it is one method of many to model past landscapes, to answer where we came 
from, and to see far better where we are going. 



Adams, R. and Wood, E. (1982) "Ancient Maya canals: grids and lattices in the Maya jungle," 

Archaeology, 35 (6): 28-35. 
Adams, R.E.W., Brown, W.E. and Culbert, J.R (1981) "Radar mapping, archaeology, and ancient 

Maya land use," Science, 213: 1457—63. 
Adams, W. (1979) "On the Argument from Ceramics to History: A Challenge Based on Evidence 

from Medieval Nubia," Current Anthropology, 20: 727—744. 
Agache, R. (1968) "Essai d'utilisation aerienne et au sol d'emulsions spectrozonales, dites 

infrarouges couleurs," Society Prehistorique de France, 65: 198-201. 
Akasheh, T.S. (2002) "Ancient and modern watershed management in Petra," Near Eastern 

Archaeology, 65 (4): 220-4. 
Alcock, S. and Cherry, J. (2004) Side by Side Survey: Comparative Regional Studies in Mediterranean 

Survey, Oxford: Oxbow Books. 
Aldenderfer (2003) "Preliminary stages in the development of a real-time digital recording 

system for archaeological excavation using Arc View GIS," Journal of GIS in Archaeology, 1: 

Alizadeh, A., Kouchoukos, N., Wilkinson, T., Bauer, A. and Mashkour, M. (2004) "Human- 
environment interactions on the Upper Khuzestan Plains, Southwest Iran, recent investiga- 
tions," Paleorient, 30: 69-88. 
Alizadeh, K. and Ur, J. (2007) "Formation and destruction of pastoral and irrigation landscapes 

on the Mughan Steppe, north-western Iran," Antiquity, 81 (311): 148-60. 
Allan, J. and Richards, T. (1983) "Use of Satellite Imagery in Archaeological Surveys," Libyan 

Studies, 14: 4-8. 
Altaweel, M. (2005) "The use of ASTER satellite imagery in archaeological contexts," 

Archaeological Prospect ion, 12: 151—66. 
Anon. (1992) "Satellite imagery reveals secrets of the past: using Landsat and Seasat in 

archaeology," Photonics Spectra, 26 (8): 114. 
Anon. (1995) "Angkor by satellite," Athena Review, 1: 12-13. 
Anon. (2003) "Archaeology: what lies beneath?," The Economist, 369: 75—6. 
Anon. (2004) "SERDP projects advance state of the science for detecting archaeological sites," 

SERDP Information Bulletin: 2. 
Anon. (2005) NASA Worldwind forums, Worldwind vs. Google Earth Beta, online at http://, accessed Sep. 15th, 2008. 
Anon. (2008) Indian court asked to ban Google Earth, from The Times online, 

December 10, 2008, 

article53l4085.ece, accessed Jan 8th, 2008 
Argotte-Espino, D. and Chavez, R.E. (2005) "Detection of possible archaeological pathways in 

Central Mexico through digital processing of remote sensing images," Archaeological Prospection, 

12 (2): 105-14. 



Arnold, J. B.I. (1993) "Matagorda Bay surveys: Applications of inexpensive satellite navigation," 

International Journal of Nautical Archaeology, 22 (1): 79-87. 
Aufrecht, W.E. {199 A) Archaeological Survey of the Kerak Plateau, Conducted During 1978-1982 

Under the Direction ofJ.M. Miller andJ.M. Pinkerton, by J.M. Miller. Reviewed in: Journal of 

the American Oriental Society, 114 (1): 133. 
Ayuga, J.G.R., Mozota, F.B., Lopez, R. and Abadia, M.F. (2006) "Application of hyperspectral 

remote sensing to the Celtiberian city of Segeda," in S. Campana and M. Forte (eds) From Space 

to Place: 2nd International Conference on Remote Sensing in Archaeology ', Oxford: British Archaeology 

Badea, A., Reif, A. and Rusdea, E. (2002) "The remote sensing imagery as support of the 

thematic data within the 'Apuseni' project," in B. Warmbein (ed.) Proceedings of the Conference 

Space Applications for Heritage Conservation, Strasbourg: European Space Agency. 
Bailey, D.W., Tringham, R., Bass, J., Stevanovich, M., Hamilton, M., Neumann, H., Angelova, I. 

and Raduncheva, J. (1998) "Expanding the dimensions of early agricultural tells: Podgoritsa 

archaeological project, " Journal of Field Archaeology, 25 (4): 373-96. 
Balaji, K., Suresh, L.S., Raghavswamy, V. and Gautam, N.C. (1996) "The use of remote sensing in 

monitoring changes in and around archaeological monuments: a case study from Hyderabad, 

Andhara Pradesh," Man and Environment , 21 (2): 63-70. 
Ballard, R. (2007) "Archaeological oceanography," in J. Wiseman and F. El-Baz (eds) Remote 

Sensing in Archaeology , Interdisciplinary Contributions to Archaeology book series, New York: 

Banks, E.P. (1995) "Remote sensing and the archaeology of the silk road," Current Archaeology, 

36 (3): 520. 
Banning, E.B. (1996) "Highlands and lowlands: problems and survey frameworks for rural 

archaeology in the Near East," Bulletin of the American Schools of Oriental Research, 301: 

Barasino, E. and Helly, B. (1985) "Remote sensing and archaeological research in Thessaly 

(Greece) New prospects in 'archaeological landscape,' in S. Campana and M. Forte (eds) 

From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: 

British Archaeology Reports. Proceedings of the EARScL/ESA Symposium European Remote Sensing 

Opportunities Strasbourg, March 31 -April 3, ESA SP-233. 
Barcelo, J.A., Forte, M. and Sanders, D.H. (2000) "The diversity of archaeological virtual worlds," 

in J. A. Barcelo, M. Forte, and D.B. Sanders (eds) Virtual Reality in Archaeology, Oxford: 

Barlindhaug, S., Holm-Olsen, M. and Tommervik, H. (2007) "Monitoring archaeological sites 

in a changing landscape-using multitemporal satellite remote sensing as an early warning 

method for detecting regrowth processes," Archaeological Prospection, 14: 231—44. 
Barta, M. and Bruna, V. (2005) "Satellite imaging in the pyramid fields," Egyptian Archaeology, 

26: 3-6. 
Beazeley, G.A. (1919) "Air photography in archaeology," The Geographical Journal, 53: 

Beazeley, G.A. (1920) "Surveys in Mesopotamia during the war," The Geographical Journal, 55: 

Beck, A. (2006) "Google Earth and World Wind: remote sensing for the masses?," Antiquity, 

80 (308). Online. Available HTTP: <> (accessed 10 

September 2008). 
Beck, A. , Philip, G. , Abdulkarim, M. and Donoghue, D. (2007) "Evaluation of Corona and Ikonos 

high resolution satellite imagery for archaeological prospection in western Syria," Antiquity, 

81(311): 161-75. 
Beck, A., Philip, G., Donoghue, D. and Galiatsatos, N. (2002) "Evaluation of integrated high and 

medium scale satellite imagery to archaeology in semi-arid environments," in B. Warmbein 



(ed.) Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 

Space Agency. 
Becker, H. (2001) "Duo- and quadro- sensor configuration for high-speed/high-resolution mag- 
netic prospecting with caesium magnetometry," Magnetic Prospecting in Archaeological Sites, 

ICOMOS: Paris, 20-25. 
Becker, H. and Fassbinder, J. (1999) "In search of Piramesses — the lost capital of Ramses II 

in the Nile Delta (Egypt) by caesium magnetometry," Archaeological Prospection, 6(4) 108: 

Becker, H. and Fassbinder, J. (2001) "In search for the city wall of Homer's Troy-development of 

high-resolution caesium magnetometry, 1992—1994," in K.H. Hemmeter, J.W.E. Fassbinder, 

W.E. Irlinger, M. Petzet and J. Ziesemer (eds) Magnetic Prospecting in Archaeological Sites, 

Munchen: Lipp Verlag. 
Behrens, C. and Sever, T. (eds) (1991) Applications of Space-age Technology in Anthropology , Stennis 

Space Center, MS: NASA. 
Belvedere, O., Papa, M.A., Ceraulo, A., Lauro, D. and Burgio, A. (2006) "GIS and Web mapping 

of S. Leonardo Valley and Alesa Hinterland," in S. Campana and M. Forte (eds) From Space to 

Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: British Archaeology 

Ben-Dor, E., Portugali, J., Kovachi, M., Shionim, M. and Vinitzki, L. (1999) "Airborne thermal 

video radiometry and excavation planning at Tel Leviah, Golan Heights, Israel," Journal of 

Field Archaeology, 26 (2): 117-27. 
Ben-Dor, E., Kovachi, M., Vinitzki, L., Shionim, M. and Portugali, J. (2001) "Detection of 

buried ancient walls using airborne thermal video radiometry," International Journal for Remote 

Sensing, 22 (18): 3689-702. 
Berry, J. (2003) "Historic Sanborn maps in the digital age: city of New Orleans," Journal of GIS 

in Archaeology , 1: 73-8. 
Bertocci, S. and Parrinello, S. (2006) "The Flaminian way in Umbria: an integrated survey project 

for the study and conservation of the historical, architectural, and archaeological features," in 

S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing 

in Archaeology , Oxford: British Archaeology Reports. 
Bevan, A. and Conolly, J. (2002) "GIS, archaeological survey, and landscape archaeology on the 

Island of Kythera, Greece," Journal of Field Archaeology, 29 (1-2): 123-38. 
Bewley, R. (2001) "Understanding England's historic landscapes: an aerial perspective," 

Landscapes, 2 (1): 74-84. 
Bewley, R. and Raczkowski, W. (2002) (eds) Aerial archaeology: developing future practice. NATO 

Science Series, New York: Springer. 
Bewley, R. and Musson, C. (2006) "Culture 2000 project. European Landscapes: past present 

and future," in S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference 

on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 
Bewley, R., Crutchley, S. and Shell, C. (2005) "New Light on an Ancient Landscape: LIDAR 

survey in the Stonehenge World," Antiquity, 79: 636-47. 
Bezori, G., Astori, B. and Guzzetti, F. (2002) "GPS and photogrammetric methodologies for an 

archaeological survey," in B. Warmbein (ed.) Proceedings of the Conference Space Applications for 

Heritage Conservation, Strasbourg: European Space Agency. 
Bhattacharya, A. (2004) "Applications of remote sensing and GIS for locating archaeological 

sites in quaternary alluvial terrain," in C. Wang (ed.) International Conference on Remote Sensing 

Archaeology, Beijing: Chinese Center for Remote Sensing Archaeology. 
Bietak, M. (1975) Tell el-Dab' a II, der fundort im rahmen einer archaologisch-geographischen 

untersuchung uber das agyptische Ost delta, Vienna: Academie der Wisseschaften. 
Bintliff, J. and Snodgrass, A. (1988) "Off-site pottery distributions: a regional and interregional 

perspective," Current Anthropology, 29 (3): 506—13. 



Birnbacher, U. and Koudelka, O. (2002) "Available satellite technology for distributing 

educational programmes," in B. Warmbein (ed.) Proceedings of the Conference Space Applications 

for Heritage Conservation, Strasbourg: European Space Agency. 
Blakely, J. A. and Horton, F. (2001) "On site identifications old and new: the example of 

Tell el-Hesi," Near Eastern Archaeology ', 64 (1-2): 24-36. 
Blank, J. E. (1973) The Impact of the Natural Sciences on Archaeology, by T.E. Allibone (ed.) Reviewed 

in: American Anthropologist, 75 (6): 1930—1. 
Blom, R., Zairins, J., Clapp, N. and Hedges, G.R. (1997) "Space technology and the discovery 

of the lost city of Ubar," 1997 IEEE Aerospace Conference Proceedings, 1: 19-28. 
Blom, R.G. (1992) "Space technology and the discovery of Ubar," Point of Beginning, August- 
September: 11-20. 
Blom, R.G., Chapman, B., Podest, E. and Murowchick, R. (2000) "Applications of remote 

sensing to archaeological studies of early Shang civilization in Northern China," Proceedings of 

the IGARSS 2000 Geoscience and Remote Sensing Symposium, 6: 2483-5. 
Blom, R.G., Crippen, R.E., Zairns, J. and Hedges, G.R. (2000) "Remote sensing, shuttle radar 

topographic mapper data, and ancient frankincense trade routes," Proceedings of the IGARSS 

2000 Geoscience and Remote Sensing Symposium, 6: 2477-9- 
Blom, R.G., Crippen, R., Elachi, C, Clapp, N., Hedges, G. and Zarins, J. (2007) "Southern 

Arabian Desert trade routes, frankincense, myrrh, and the Ubar legend," in J. Wiseman and 

F. El-Baz (eds) Remote Sensing in Archaeology, Interdisciplinary Contributions to Archaeology 

book series, New York: Springer. 
Bloxam, E. and Storemeyr, P. (2002) "Old Kingdom basalt quarrying activities at Widan el Faras, 

northern Fayum deserts," Journal of Egyptian Archaeology , 88: 23-36. 
Blumberg, D.G. (1998) "Remote sensing of desert dune forms by polarimetric synthetic aperture 

radar (SAR)," Remote Sensing of Environment , 65: 204—216. 
Blumberg, D.G., Freilikher, V., Fuks, I., Kaganovskii, Yu., Maradudin, A. A. and Rosenbluh, M. 

(2002) "Effects of roughness on the retroreflection from dielectric layers," Waves in Random 

Media, 12 (3): 279-292. 
Boehler, W., Heinz, G., Qiming, G. and Shenping, Y. (2004) "The progress in satellite imaging 

and its application to archaeological documentation during the last decade," in C. Wang 

(ed.) International Conference on Remote Sensing Archaeology , Beijing: Chinese Center for Remote 

Sensing Archaeology. 
Bofinger, J., Kurz, S. and Schmidt, S. (2006) "Ancient maps— modern data sets different inves- 
tigative techniques in the landscape of the Early Iron Age Princely Hill Fort Heuneburg, 

Baden-Wurttemberg, Germany," in S. Campana and M. Forte (eds) From Space to Place: 2nd 

International Conference on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 
Bogdanos, M. (2005) "The casualties of war: the truth about the Iraq museum," American Journal 

of Archaeology, 109: 477-526. 
Bolten, A., Bubenzer, O. and Darius, F. (2006) "A digital elevation model as a base for the 

reconstruction of Holocene land-use potential in arid regions," Geoarchaeology, 21 (7): 75 1—62. 
Bourgeois, J. and Marc, M. (eds) (2003) Aerial Photography and Archaeology 2003: A Century of 

Information, Gent: Academia Press. 
Box, P. (2003) "Safeguarding the Plain of Jars: megaliths and unexploded ordnance in the Lao 

People's Democratic Republic, " Journal of GIS in Archaeology, 1: 90-102. 
Bradford, J.S.P. (1956) Mapping Two Thousand Tombs from the Air: How Aerial Photography Plays 

its Part in Solving the Riddle of the Etruscans, London: Illustrated London News. 
Brand, P. (2001a) "Rescue epigraphy in the Hypostyle Hall, a postscript to 'A forest of columns: 

the Karnak Great Hypostyle Hall Project', by William J. Murnane," KMT: A Modern Journal 

of Ancient Egypt, 12 (3): 59- 
Brand, P. (2001b) "Rescue epigraphy in the Karnak Hypostyle Hall," Egyptian Archaeology, 

19: H-13. 



Brewer, D., Wenke, R., Isaacson, J. and Haag, D. (1996) "Mendes regional archaeological survey 

and remote sensing analysis," Sahara, 8: 29-42. 
Brivio, P. A., Pepe, M. and Tomason, R. (2000) "Multispectral and multiscale remote sensing 

data for archaeological prospecting in an alpine alluvial plain ," Journal of Cultural Heritage, 

1: 155-64. 
Brophy, K. and Cowley, D. (2005) From the Air: Understanding Aerial Archaeology ■, Stroud: The 

History Press. 
Broucke, P.B.F.J. (1999) Virtual Archaeology: Re-Creating Ancient Worlds, by Maurizio Forte and 

Alberto Siliotti and Marmaria: he sanctuaire d Athena a Delphes, by J. Bommelaer. Reviewed 

in: American Journal of Archaeology, 103 (3): 539—40. 
Brunner, U. (1997) "Geography and human settlements in ancient southern Arabia," Arabian 

Archaeology and Epigraphy , 8 (2): 190-202. 
Bubenzer, O. and Bolten, A. (2003) "In Egypt, Sudan, and Namibia, GIS helps compare human 

strategies of coping with arid habitats," Archnews, Summer 2003: 13-14. 
Bubenzer, O. and Riemer, H. (2007) "Holocene climatic change and human settlement 

between the central Sahara and the Nile Valley: archaeological and geomorphological results," 

Geoarchaeology, 22 (6): 607-20. 
Buettner-Januch, J. (1954) "Use of infrared photography in archaeological work," American 

Antiquity, 20: 84-7. 
Bugayevskiy, L. and Snyder, J (1995) Map Projections: A Reference Manual, London: Taylor & 

Butzer, K. (1975) "Delta," Lexikon der Agyptologie, 1: 1043-52. 
Butzer, K. (1976) Early Hydraulic Civilization in Egypt: A Study in Cultural Ecology, Chicago: 

University of Chicago Press. 
Butzer, K. (2002) "Geoarchaeological implications of recent research in the Nile Delta," in 

CM. van den Brink and T.E. Levy (eds) Egypt and the Levant: Interrelations from the 4th Through 

the Early 3rd Millennium BCE, Leicester: Leicester University Press. 
Campana, S. (2002) "High resolution satellite imagery: a new source of information to the 

archaeological study of Italian landscapes? Case study of Tuscany," in B. Warmbein (ed.) 

Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 

Space Agency. 
Campana, S. (2007) "Understanding archaeological landscapes: steps towards an improved 

integration of survey methods in the reconstruction of subsurface sites in South Tuscany," 

in J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology, New York: Springer. 
Campana, S. and Forte, M. (eds) (2006) From Space to Place: 2nd International Conference on Remote 

Sensing in Archaeology, Oxford: British Archaeology Reports. 
Campana, S. and Frezza, B. (2006) "A proposal for the digital storage and sharing of remotely 

sensed archaeological data," in S. Campana and M. Forte (eds) From Space to Place: 2nd 

International Conference on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 
Campana, S. and Francovich, R. (2005) Seeing the Unseen. Buried Archaeological Landscapes in 

Tuscany, Taylor & Francis, The Netherlands. 
Campana, S., Francovich, R. and Marasco, M. (2006) "Remote sensing and ground-truthing 

of a medieval mound (Tuscany-Italy)," in S. Campana and M. Forte (eds) From Space to 

Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: British Archaeology 

Campbell, K.M. (1981) "Remote Sensing: Conventional and Infrared Imagery for Archaeolo- 
gists," University of Calgary Archaeology Association, 11: 1-8. 
Cantral, R.D. (1975) "A Satellite Perspective of the Yucatan Peninsula," Proceedings of the Central 

States Anthropological Society, 1: 83—94. 
Capper, J.E. (1907) "Photographs of Stonehenge as seen from a war balloon," Archaeologia, 

60: 571. 



Carr, T. and Turner, M. (1996) "Investigating regional lithic procurement using multispectral 
imagery and geophysical exploration," Archaeological Prospection, 3: 109-27 . 

Cashman, J. (2001) "Egypt's new archaeological database," Archaeology Computing Newsletter, 
Spring: 48-9- 

Castleford, J. (1992) "Archaeology, GIS and the time dimension: an overview," in G. Lock and 
J. Moffett (eds) Computer Applications and Quantitative Methods in Archaeology \ Oxford: Tempus 

Caton-Thompson, G. (1929) "Zimbabwe," Antiquity, 3: 424-33. 

Caton-Thompson, G. (1931) "Kharga Oasis," Antiquity, 5: 221-6 

Cerasetti, B. and Massino, M. (2002) "The Murghab delta palaeochannel: reconstruction on the 
basis of remote sensing from space," in B. Warmbein (ed.) Proceedings of the Conference Space 
Applications for Heritage Conservation, Strasbourg: European Space Agency. 

Challis, K. (2006) "Airborne laser altimetry in alluviated landscapes," Archaeological Prospection, 
13 (2): 103-27. 

Challis, K. and Howard, A.J. (2006) "A review of trends within archaeological remote sensing 
in alluvial environments," Archaeological Prospection, 13 (4): 231—40. 

Challis, K., Howard, A.J., Moscrop, D., Gearey, B., Smith, D., Carey, C. and Thompson, A. 
(2006) "Using airborne LIDAR intensity to predict the organic preservation of waterlogged 
deposits," in S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference on 
Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 

Challis, K., Priestnall, G., Gardner, A., Henderson, J. and O'Hara, S. (2002) "Corona remotely- 
sensed imagery in dryland archaeology: the Islamic city of al-Raqqa, Syria," Journal of Field 
Archaeology, 29 (1-2): 139-53. 

Changlin, W. and Ning, Y. (2004) "Environmental study and information extraction of archae- 
ological features with remote sensing imagery in arid areas of Western China," in C. Wang 
(ed.) International Conference on Remote Sensing Archaeology, Beijing: Chinese Center for Remote 
Sensing Archaeology. 

Chapman, H. (2006) Landscape Archaeology and GIS, Stroud: The History Press. 

Chapoulie, R., Martinaud, M., Paillou, P. and Dreuillet, P. (2002) "New airborne synthetic 
aperture RADAR for a new method of archaeological prospection of buried remains," in 
B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage Conservation, 
Strasbourg: European Space Agency. 

Chevallier, R., Fontanel, A., Grau, G. and Guy, M. (1970) "Application of optical filtering to 
the study of aerial photography," Photogrammetria, 26: 17—35. 

Chittick, N. (1974) Kilwa: An Islamic Trading City on the Fast African Coast. Volume I: History and 
Archaeology, Nairobi: British Institute of Eastern Africa. 

Chlodnicki, M., Fattovich, R. and Salvatori, S. (1992) "The Italian archeological mission of 
the C.S.R.L. Venice of the eastern Nile Delta: a preliminary report of the 1987-1988 
field seasons," Cahier de Recherches de ITnstitut de Papyrologie et d'Fgyptologie de Lille, 14: 
45-62. ' ' ' 

Clapp, N. (1996) The Road to Ubar, New York: Mariner Books. 

Clark, CD., Garrod, S.M. and Pearson, P. (1998) "Landscape archaeology and remote sensing in 
Southern Madagascar," International Journal of Remote Sensing, 19 (8): 1461—77. 

Cleave, R.W.L. (1985) "Satellite mapping and the interpretation of 'false color,' " The Biblical 
Archaeologist, 48 (3): 160-1. 

Cline, E. (2007) From Fden to Exile: Unraveling Mysteries of the Bible, Washington, DC: National 

Cole, J., Feder, K, Harrold, E, Eve, R., and Kehoe, A. (1990) "On folk archaeology in 
anthropological perspective," Current Anthropology, 31 (4): 390—4. 

Coleman, D.F., Ballard, R.D. and Gregory, T. (2003) "Marine archaeological exploration of the 
Black Sea," Proceedings of OCEANS 2003, 3: 1287-91. 



Comer, D. (1999) "Discovering archaeological sites from space: using shuttle radar at Petra, 

Jordan," CRM, 2 1(5): 9-1 1 . 
Comer, D. and Blom, R. (2002) Detection and identification of archaeological sites 

and features using RADAR data acquired from airborne platform. Online. Available 

HTTP: <> (accessed 4 

September 2008) 
Comer, D. and Blom, R. (2007) "Detection and identification of archaeological features using 

synthetic aperture radar (SAR) data collected from airborne platforms," in J. Wiseman and 

F. El-Baz (eds) Remote Sensing in Archaeology ', Interdisciplinary Contributions to Archaeology 

book series, New York: Springer. 
Comfort, A. (1997 a) Mission Archeologique de Zeugma, satellite remote sensing and archaeological survey 

on the Euphrates: remote sensing and archaeological survey on the Euphrates report, Luxembourg: 

Comfort, A. 
Comfort, A. (1997b) "Satellite remote sensing and archaeological survey on the Euphrates," 

Archaeological Computing Newsletter , 48: 1-8. 
Comfort, A. (1997c) "Satellite remote sensing and archaeological survey on the Euphrates," Aerial 

Archaeology Research Group News, 14: 39-46. 
Comfort, A. ( 1 998) Mission Archeologique de Zeugma, satellite remote sensing and archaeological survey on 

the Euphrates: remote sensing and archaeological survey on the Euphrates report, Luxembourg: Comfort. 
Comfort, A. ( 1 999) Mission Archeologique de Zeugma, satellite remote sensing and archaeological survey on 

the Euphrates: remote sensing and archaeological survey on the Euphrates report, Luxembourg: Comfort. 
Comfort, A. and Ergec, R. (2001) "Following the Euphrates in antiquity: north— south routes 

around Zeugma," Anatolian Studies, 51: 19-49- 
Comfort, A., Abadie-Reynal, C. and Ergec, R. (2000) "Crossing the Euphrates in antiquity: 

Zeugma seen from space," Anatolian Studies, 50: 99—126. 
Conant, F. (1990) "1990 and beyond: satellite remote sensing and ecological anthropology," in 

Emilio F. Moran (ed.) The Ecosystem Approach in Anthropology from Concept to Practice, Ann Arbor: 

University of Michigan Press. 
Condit, H.R. (1970) "The spectral reflectance of American soils," Photogrammetric Engineering, 

Connah, G. and Jones, A. (1983) "Aerial archaeology in Australia," Aerial Archaeology, 9\ 1—23. 
Conolly, J. and Lake, M. (2006) Geographical Information Systems in Archaeology , Cambridge Manuals 

in Archaeology book series, Cambridge: Cambridge University Press. 
Conroy, G., Anemone, A., Regenmorter, J. and Addison, A. (2008) "Google Earth, GIS, and the 

Great Divide: A new and simple method for sharing paleontological data," Journal of Human 

Evolution, 2008, in press. 
Conyers, L. (2004) Ground-Penetrating Radar for Archaeology (Geophysical Methods for Archaeology), 

Lanham, MD: AltaMira Press. 
Conyers, L. (2007) "Ground-penetrating radar for archaeological mapping," in J. Wiseman and 

F. El-Baz (eds) Remote Sensing in Archaeology , Interdisciplinary Contributions to Archaeology 

book series, New York: Springer. 
Cooper, E, Bauer, M. and Cullen, B. (1991) Satellite spectral data in archaeological reconnaissance 

in western Greece, in T.S.C. Behrens (ed.) Applications of Space-Age Technology in Anthropology, 

Stennis Space Center: NASA. 
Coutellier, V. and Stanley, D. J. (1987) "Late Quaternary stratigraphy and paleogeography of the 

eastern Nile Delta," Marine Geology, 27: 257—75. 
Couzy, A. (1985) "Environmental studies using satellite imagery," Studies in the History and 

Archaeology of Jordan, 2: 287—90. 
Cox, C. (1992) "Satellite imagery, aerial photography and wetland archaeology: an interim report 

on an application of remote sensing to wetland archaeology: the pilot study in Cumbria, 

England," World Archaeology , 24 (2): 249-67. 



Crashaw, A. (2001) "Some history of the captured German WWII photography," Aerial 
Archaeology Research Group Newsletter, 22: 45-9- 

Crawford, O.G.S. (1923a) "Stonehenge from the air: course and meaning of 'The Avenue'," 
Observer: 13. 

Crawford, O.G.S. (1923 b) "What air photography means to the future of archaeology," Christian 
Science Monitor: 9. 

Crawford, O.G.S. and Keillor, A. (1928) Wes sex from the Air, London: Clarendon Press. 

Custer, J., Eveleigh, T., Klemas, V. and Wells, I. (1986) "Application of LANDSAT Data and 
synoptic remote sensing to predictive models for prehistoric archaeological sites: an example 
from the Delaware coastal plain," American Antiquity, 51: 572—88. 

Darnell, J. (2002) Theban Desert Road Survey in the Egyptian Western Desert .Volume 1: Gebel Tjauti 
Rock Inscriptions 1-45 and Wadi el-Hol Rock Inscriptions 1-45, Chicago: Oriental Institute 

Davenport, G.C. (2001) "Remote sensing applications in forensic investigations," Historical 
Archaeology, 35 (1): 87-100. 

Davidson, D. and Shackley, M. (1985) Geoarchaeology: Earth Science and the Past, London: 

Davis, J.L. (1993) Interpreting Space: GIS and Archaeology, by K.M.S. Allen, S.W. Green and 
E.B.W. Zubrow. Reviewed in: American Journal of Archaeology, 97 (2): 357—9- 

Dayalan, D. (2006) "The use of remote sensing and GIS in the management and conserva- 
tion of heritage properties at the Agra," in S. Campana and M. Forte (eds) From Space to 
Place: 2nd International Conference on Remote Sensing in Archaeology, Oxford: British Archaeology 

De Laet, V., Pulissen, E. and Waelkens, M. (2007) "Methods for the extraction of archaeolog- 
ical features from very high-resolution Ikonos-2 remote sensing imagery, Hisar (Southwest 
Turkey)," Journal of Archaeological Science, 34 (5): 830-41. 

De Maeyer, P., Bogaert, P., de Man, J., de Temmerman, L., Gamanya, R., Binard, M. and Muller, F. 
(2002) "Cartography and land use change of world heritage areas and the benefits of remote 
sensing and GIS for conservation," in B. Warmbein (ed.) Proceedings of the Conference Space 
Applications for Heritage Conservation, Strasbourg: European Space Agency. 

Deegan, A. and Foard, G. (2008) Mapping Ancient Landscapes in Northamptonshire, London: English 

Dengfeng, C. and Dengrong, Z. (2004) "Modeling and rending realistic terrain from pho- 
tographs," in C. Wang (ed.) Proceedings of the International Conference for Remote Sensing 
Archaeology, Beijing: Center for Remote Sensing Archaeology. 

Deo, S.G. and Joglekar, P.P. (1996) "Satellite remote sensing in archaeology," Man and 
Environment, 21 (2): 59—62. 

Deroin, J. P. and Verger, F. (2002) "Application of remote sensing to monitor the Mont-Saint- 
Michel Bay (France): natural and cultural heritage aspects," in B. Warmbein (ed.) Proceedings 
of the Conference Space Applications for Heritage Conservation, Strasbourg: European Space Agency. 

Deroin, J.P., Tereygeol, E, Benoit, P., Al-Thari, M., Al-Ganad, I.N., Heckes, J., Hornschuch, A., 
Peli, A., Pillault, S. and Florsch, N. (2006) "Archaeological remote sensing in Yemen, the 
Jabali test site: from large scale survey to field investigation," in S. Campana and M. Forte 
(eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: 
British Archaeology Reports. 

Deuel, L. (1973) Flights into Yesterday: The Story of Aerial Archaeology , Harmondsworth: Penguin. 

Dingwall, L., Barratt, G., Fitch, S. and Huckerby, C. (2002) "The use of remotely-sensed imagery 
in cultural landscape characterization at Fort Hood, Texas," in B. Warmbein (ed.) Proceedings 
of the Conference Space Applications for Heritage Conservation, Strasbourg: European Space Agency. 

Dolphin, L., Moussa, A. and Mokhtar, G. (1977) Applications of Modern Sensing Technology to 
Egyptology, Menlo Park: SRI International Radio Physics Laboratory. 



Doneus, M. (2001) "Precision mapping and interpretation of oblique aerial photographs," 

Archaeological Prospection ,8:1 3-2 7 . 
Doneus, M. and Briese, C. (2006a) "Digital terrain modeling for archaeological interpreta- 
tion within forested areas using full-waveform laserscanning," in M. Ioanides, D. Arnold, 

F. Niccolucci and K. Mania (eds) The 7th Symposium on Virtual Reality, Archaeology, and Intelligent 

Cultural Heritage VAST 2006, Wellesley, MA: AK Peters. 
Doneus, M. and Briese, C. (2006b) "Full-waveform airborne laser scanning as a tool for archaeo- 
logical reconnaissance," in S. Campana and M. Forte (eds) From Space to Place: 2nd International 

Conference on Remote Sensing in Archaeology ', Oxford: British Archaeology Reports. 
Donoghue, D.N.M. and Shennan, I. (1987) "Remote sensing of Morton Fen, Lincolnshire: 

summary of recent work," Fen land Research, 4: 46-53. 
Donoghue, D.N.M. and Shennan, I. (1988) "The application of multispectral remote sensing 

techniques to wetland archaeology," in P. Murphy and C. French (eds) The Exploitation of 

Wetlands, Oxford: British Archaeological Reports. 
Donoghue, D.N.M., Galiatsatos, N., Philip, G. and Beck, A.R. (2002) "Satellite imagery for 

archaeological applications: a case study from the Orontes Valley, Syria," in R.H. Brewley and 

W. Raczkowski (eds) Aerial Archaeology: Developing Future Practice, NATO Science book series, 

vol. 1, Amsterdam: IOS Press. 
Dorsett, J., Gibertson, D., Hunt, C. and Barker, G. (1984) "The UNESCO Libyan Valleys survey 

VIII: image analysis of Landsat data for archaeological and environmental surveys," Libyan 

Studies, 15: 71-80. 
Drager, D.L. (1983) "Projecting archaeological site concentrations in the San Juan Basin, New 

Mexico," in D.L. Drager and T. R. Lyons (eds) Remote Sensing in Cultural Resource Management, 

Washington, DC: National Park Service. 
Dubois, J.M.M. (1996) Archaeology, Volcanism, and Remote Sensing in the Arenal Region. Costa Rica, 

by P.D. Sheets and B.R. McKee (eds). Reviewed in: International Journal of Remote Sensing, 

17(1): 214-15. 
Ebert, J.I. (1976) "Remote sensing within an archaeological research framework: methods, 

economics, and theory," in T.R. Lyons and R.K. Hitchcock (eds), Remote Sensing Tech- 
niques in Archaeology , Albuquerqe, NM: Chaco Center, National Park Service, University of 

New Mexico. 
Ebert, J.I. and Blumenschine, R. (1999) "Bones and stones," GPS World, 10: 20-29- 
Ebert, J.I. and Gutierrez, A. (1979) "Relationships between landscapes and archaeological sites in 

Shenandoah National Park: a remote sensing approach," Bulletin of the Association for Preservation 

Technology, 11 (4): 69-87. 
Ebert, J.I. and Lyons, T.R. (1980a) "Remote sensing in archaeology, cultural resources treatment 

and anthropology: The United States of America in 1979," Aerial Archaeology, 5: 1—19- 
Ebert, J.I. and Lyons, T.R. (1980b) "The detection, mitigation, and analysis of remotely-sensed, 

'ephemeral' archaeological evidence," in T.R. Lyons and F.J. Mathien (eds) Cultural Resources 

Remote Sensing, Washington, DC: National Park Service. 
Ebert, J.I., Lyons, T.R. and Drager, D.L. (1979) "Comments on 'Application of orthophoto 

mapping to archaeological problems,' " American Antiquity, 44 (2): 341—5. 
Edeine, B. (1956) "Une methode praqtique pour la detection aerienne des sites archaeologiques, 

en particulier par la photographie sur films en coleurs et sur films infrarouges," Bulletin de la 

Societe Prehistorique Fracaise, 53: 540—546. 
Edwards, A. (1891) Pharaohs, Fellahs, and Explorers, London: Mcllvane and Co. 
Edwards, D.A. and Partridge, C. (1977) Aerial Archaeology, London: Aerial Archaeology 

Eichhorn, B., Hendricks, S., Riemer, H. and Stern, B. (2005) "Desert roads and transport vessels 

from late Roman-Coptic times in the Eastern Sahara," Journal of African Archaeology , 3 (2): 




Eisenbeiss, H. and Zhang, L. (2006) "Comparison of DSMs generated from mini UAV imagery 
and terrestrial laser scanner in a cultural heritage application," International Archives of 
Photogrammetry, Remote Sensing and Spatial Information Sciences, 36 (5): 90-6. 

Eisenbeiss, H., Lambers, K. and Sauerbier, M. (2006) "Photogrammetric recording of the archae- 
ological site of Pinchango Alto (Peru), using a mini helicopter (UAV)," in A. Figueiredo and 
G.L. Velho (eds) The World is in Your Eyes- Proceedings of the 33rd CAA Conference, Tomar, 
Portugal, Lisbon: Instituto Portuges de Arquelogia. 

Eisenbeiss, H., Lambers, K., Sauerbier, M. and Zhang, L. (2005) "Photogrammetric 
documentation of an archaeological site (Palpa, Peru) using an autonomous model helicopter," 
International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34 
(5): 238-243. 

Elachi, C. (1982) "Radar images of earth from space," Scientific American, 247: 46-53. 

El-Aref, N (2008) "Home from the range," Al-Ahram, 25-31 December 2008 Issue No. 927, 
online at, accessed Jan 8th, 2008. 

El-Baz, F. (1980) "Narrative of the journey," The Geographical Journal, 146 (1): 51-9- 

El-Ba2, F. (1984) "The desert in the space age," in F. El-Ba2 (ed.) Deserts and Arid Lands , The 
Hague: Martinus Nijhoff Publishers. 

El-Ba2, F. (1989) "Monitoring Lake Nasser by space photography," in A. Rango (ed.) Remote 
Sensing and Large Scale Global Processes, Baltimore: International Association of Hydrological 

El-Baz, F, Robinson, C. and Al-Saud, T.S.M. (2007) "Radar images and geoarchaeology of the 
eastern Sahara" in J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology , Interdisciplinary 
Contributions to Archaeology book series, New York: Springer. 

El-Etr, H., Yousif, M. and Dardir, A. (1979) "Utilization of 'Landsat'images and conventional 
aerial photographs in the delineation of some aspects of the geology of the central eastern 
desert, Egypt," Annals of the Geological Survey of Egypt, 9: 136-62. 

El-Gamily, H., Nasr, S. and E-Raey, M. (2001) "An assessment of natural and human-induced 
changes along Hurgada Ras Abu Soma coastal area, Red Sea," International J ournal of Remote 
Sensing, 22: 2999-3014. 

El-Ghandour, M. (2003) New Regulations to Foreign Missions, Cairo: Egyptian Supreme Council 
for Egyptian Antiquities, Ministry of Culture, distributed through the American Research 
Center in Egypt. 

El-Raey, M., El-Din, M.S., Khafagy, A. and Zed, A. (1999) "Remote sensing of beach ero- 
sion/accretion patterns along Damietta-Port Said shoreline, Egypt," International Journal of 
Remote Sensing, 20: 1087-1106. 

El-Raey, M., Frihy, O., Nasr, S. and El-Hattab, M. (1995) "Change detection of Rosetta 
promontory over the last forty years," International Journal of Remote Sensing, 16 (5): 825—34. 

El-Rakaiby, M., Ashmawy, M., Yehia, M., and Ayoub, A. (1994) "In-situ reflectance measure- 
ments and TM data of some sedimentary rocks with emphasis on white sandstone, southwestern 
Sinai, Egypt," International Journal for Remote Sensing, 15 (18): 3785—97. 

Emberling, G. (1996) Settlement Development in the North Jazira, Iraq: A Study of the Archae- 
ological Landscape, by T.J. Wilkinson and D.J. Tucker. Reviewed in: American Antiquity, 
61 (4): 820. 

Empereur, J. (2000) Alexandria rediscovered, New York: George Braziller. 

Engelbach, R. (1929) "The aeroplane and Egyptian archaeology," Antiquity, 3 (12): 47—73. 

Ernenwein, E.G. and Kvamme, K.L. (2008) "Data processing issues in large-area GPR surveys: 
correcting trace misalignments, edge discontinuities and striping," Archaeological Prospection, 
15: 133-49. 

Estrada-Belli, F. and Koch, M. (2007) "Remote sensing and GIS analysis of a Maya city and 
its landscape: Holmul, Guatemala," in J. Wiseman and F. El-Baz (eds), Remote Sensing in 
Archaeology, New York: Springer. 



Etaya, M., Sudo, N. and Sakata, T. (2000) "Detection of subsurface ancient Egyptian remains 
utilizing optical and microwave satellite data," Proceedings of the IGARSS 2000 Geoscience and 
Remote Sensing Symposium, 6: 2480-2. 

Evans, D. and Farr, T.G. (2007) "The use of interferometric synthetic aperture radar (InSAR) in 
archaeological investigations and cultural heritage preservation," in J. Wiseman and F. El-Baz 
(eds) Remote Sensing in Archaeology ', Interdisciplinary Contributions to Archaeology book series, 
New York: Springer. 

Evans, D., Pottier, C, Fletcher, R., Helmsley, S., Tapley, I., Milne, A. and Barbetti, M. (2007) 
"A comprehensive archaeological map of the world's largest preindustrial settlement complex 
at Angkor, Cambodia," Proceedings of the National Academy of Sciences of the United States, 104 
(36): 14277-82. 

Fagan, B.M. (1989) "The backward-looking curiosity: a glance at archaeology in the year of our 
Lord 1989," American Journal of Archaeology, 93 (3): 445—9- 

Failmezger, V. (2001) High resolution aerial color IR, multispectral, hyper- spectral, and 
SAR imagery over the Oatlands Plantation archaeological site near Leesburg, Virginia," 
in S. Campana and M. Forte (eds) Remote Sensing in Archaeology, Florence: All' Insegna 
Del Giglio. 

Farley, J., Limp, W. and Lockhart, J. (1990) "The archaeologist's workbench: integrating GIS, 
remote sensing, EDA and database management," in K. Allen, S. Green and E. Zubrow, 
Interpreting Space: GIS and Archaeology , Bristol, PA: Taylor and Francis. 

Farrow, J., Becker, F, Allenbach, B. and Clandillon, S. (2002) "Practical remote sensing activities 
in an interdisciplinary masters-level space course," in B. Warmbein (ed.) Proceedings of the 
Conference Space Applications for Heritage Conservation, Strasbourg: European Space Agency. 

Fassbinder, J. and Hetu, R. (1997) "Bibliographic research on aerial archaeology an archaeological 
prospection," Aerial Archaeology Research Group News, 14: 49—51. 

Fedick, S.L. and Ford, A. (1990) "The prehistoric agricultural landscape of the central Maya 
lowlands: an examination of local variability in a regional context," World Archaeology , 22 (1): 

Fedick, S.L., Morrison, B.A., Andersen, B.J., Boucher, S. and Acosta, J.D. (2000) "Wetland 
manipulation in the Yalahau region of the northern Maya lowlands, ' : 'Journal of F 'ield Archaeology ', 
27(2): 131-52. 

Ferrarese, E, Mozzi, P., Veronese, F. and Cervo, F. (2006) High resolution DTM for the geo- 
morphological and geoarchaeological analysis of the city of Padua (Italy)," in S. Campana and 
M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , 
Oxford: British Archaeology Reports. 

Fiennes, R. (1991) Atlantis of the Sands: The Search for the host City ofUr, London: Bloomsbury. 

Fisher, J. and Fisher, B. (1999) "The use of KidSat images in the further pursuit of the frankincense 
roads to Ubar," Geoscience and Remote Sensing, 37 (4): 1841-7. 

Fletcher, R. and Evans, D. (2002) "The extent and settlement patterns of Angkor, Cambodia: 
preliminary results of an AirSAR survey in September 2000," in B. Warmbein (ed.) 
Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 
Space Agency. 

Folan, W, Marcus, J. and Miller, W.F. (1995) "Verification of a Maya settlement model through 
remote sensing," Cambridge Archaeological Journal, 5 (2): 277-83. 

Foley, B. (2008) "Greek Deep water Survey" American J ournal of Archaeology , in press. 

Forte, M. (1993) "Image processing applications in archaeology: classification systems of archae- 
ological sites in the landscape," in J. Andressen, T. Madsen and I. Scollar (eds) Computing the 
Past: Computer Applications and Quantitative Methods in Archaeology , Aarhus: Aarhus University 

Forte, M. (1998) "II Progetto Valle del Belice: applicazioni GIS e di remote sensing su dati 
archeologici," Archeologia e Calcolatori, 9: 291—304. 



Forte, M. (2000) "Archaeology and virtual micro-topography: the creation of DEMs for recon- 
structing fossil landscapes by remote sensing and GIS applications," in G. Locke (ed.) Beyond 

the Map, Amsterdam: IOS Press. 
Forte, M. (2002) "GIS, aerial photographs and microtopography in archaeology: methods and 

applications," in R.H. Brewley and W. Raczkowski (eds) Aerial Archaeology: Developing Future 

Practice, NATO Science book series, vol. 1, Amsterdam: IOS Press. 
Fowler, M. (1993) "Stonehenge from space," Spaceflight, 35: 130-2. 
Fowler, M. (1994a) "Danebury and its environs from 830 kilometers," Hampshire Field Club and 

Archaeological Society Section Newsletter, 21: 26-30. 
Fowler, M. (1994b) "Ground cover mapping from multispectral satellite imagery," Aerial 

Archaeology Research Group News, 9: 11-19- 
Fowler, M. (1994c) "Stonehenge from a new perspective: the detection of archaeological features 

on multispectral satellite imagery," Aerial Archaeology Research Group News, 10: 8—16. 
Fowler, M. (1994d) "Satellite image processing for archaeologists," Archaeological Computing 

Newsletter, 39: 2-8. 
Fowler, M. (1995 a) "High resolution Russian satellite imagery," Aerial Archaeology Research Group 

News, 10: 29-32. 
Fowler, M. (1995b) "Detection of archaeological features on multispectral satellite imagery," 

Aerial Archaeology Research Group News, 10: 7—14. 
Fowler, M. (1996a) "Declassified intelligence satellite photographs," Aerial Archaeology Research 

Group News, 13:3 0-3 5 . 
Fowler, M. (1996b) "High resolution satellite imagery in archaeological applications: a Russian 

satellite photograph of the Stonehenge region," Antiquity, 269: 667-71. 
Fowler, M. (1997a) "Declassified intelligence satellite photographs — an update," Aerial Archae- 
ology Research Group News, 14: 47—8. 
Fowler, M. (1997b) "It may not be done well ... but it could be the best that is available," Aerial 

Archaeology Research Group News, 15: 33-5. 
Fowler, M. (1999) "High resolution satellite imagery from the Internet," Aerial Archaeology 

Research Group News, 17: 29-32. 
Fowler, M. (2004) "The archaeological potential for Kh-7/9 intelligence photographs," Aerial 

Archaeology Research Group News, 26: 11—16. 
Fowler, M. and Curtis, H. (1995) "Stonehenge from 230 kilometers," Aerial Archaeology Research 

Group News, 11: 8-16. 
Fowler, M. and Darling, P. (1988) "Archaeological applications of imaging radar," Aerial 

Archaeology Research Group News, 16: 14-19- 
Fowler, M.J.F. and Fowler, Y.M. (2005) "Detection of archaeological crop marks on declassi- 
fied Corona KH-4B intelligence satellite photography of southern England," Archaeological 

Prospection, 12 (4): 257-64. 
Fowler, W., Estrada-Belli, F, Bales, J., Reynolds, M. and Kvamme, K. (2007) "Landscape 

archaeology and remote sensing of a Spanish-conquest town: Ciudad Vieja, El-Salvador," in 

J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology, Interdisciplinary Contributions 

to Archaeology book series, New York: Springer. 
Franco, L. (1996) "Ancient Mediterranean harbours: a heritage to preserve," Ocean and Coastal 

Management, 30 (2-3): 115-51. 
Freeman, A., Hensley, S. and Moore, E. (1999) "Analysis of radar images of Angkor, Cambodia," 

Proceedings of the IGARSS 1 999 International Geoscience and Remote Sensing Symposium, 5 : 2572—4. 
French, C. (2002) Geoarchaeology in action: studies in soil micromorphology and landscape evolution, 

London: Routledge. 
Frihy, O., Dewidar, K., Nasr, S. and El-Ray, M. (1998) "Change detections of the northeast Nile 

Delta of Egypt: shoreline changes, spit evolution, margin changes, of the Manzala lagoon and 

its islands," International Journal of Remote Sensing, 19 (10): 1901—12. 



Frihy, O., Nasr, S., El-Hattab, M. and El-Raey, M. (1994) "Remote sensing of beach erosion along 
Rosetta promontory northwestern Nile delta, Egypt" International Journal of Remote Sensing, 
15(8): 1649-60. 

Ganzin, N. and Mulama, M. (2002) "Evaluation of forage resources in semi-arid Savannah envi- 
ronments with satellite imagery: contribution to the management of protected areas: a case 
study on the Nakuru National Park, Kenya (Preliminary Version)," in B. Warmbein (ed.) 
Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 
Space Agency. 

Garbuzov, G.P. (2003a) "Archaeological investigations and satellite remote sensing," Rossiiskaia 
Arkheologiia, 2: 45—55. 

Garbuzov, G.P. (2003b) "The use of space remote sensing for archaeological mapping of the 
Tarman Peninsula, Russia," Archaeologia Polona, 14: 176-7. 

Gatsis, I., Pavlopoulos, A. and Parcharidis, I. (2001) "Geomorphological observations and 
related natural hazards using merged remotely sensed data: a case study in the Corinthos 
Area (NE Peloponnese, S. Greece)," Geografiska Annaler Series A, Physical Geography, 83 (4): 

Giddy, L. and Jeffreys, D. (1992) "Memphis 1991 ," Journal of Egyptian Archaeology ', 79: 1-11. 

Gillion, D.A. (1970) "Use of infrared photography in archaeology," Colorado Anthropologist, 2: 

Given, M., Knapp, A.B., Meyer, N., Gregory, T.E., Kassianidou, V., Noller, J.S., Wells, L., 
Urwin, N. and Wright, H. (1999) "The Sydney Cyprus Survey Project: an interdisciplinary 
investigation of long-term change in the North Central Troodos, Cyprus," Journal of Field 
Archaeology, 26 (1): 19-39- 

Glueck, N. (1965) Deities and Dolphins: the Story of the Nabataeans, New York: Farrar, Straus and 

Godja, M. (2004) "Aerial archaeology in Central Europe: current state of the Czech project," in 
C. Wang (ed.) Proceedings of the International Conference for Remote Sensing Archaeology , Beijing: 
Center for Remote Sensing Archaeology. 

Going, C. (2002) "A neglected asset. German aerial photography of the Second World War 
period," in R.H. Bewley and W. Raczowski (eds) Aerial Photography: Developing Future Practice, 
NATO Science Series 1: Life and Behavioural Sciences book series, vol. 337, Amsterdam: IOS 

Golchale, P. and Bapat, S. (2006) "Reconstructing the ancient republics (janapadas) of the Indian 
sub-continent," in S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference 
on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 

Goodchild, R.G. (1950) "Roman Tripolitania: reconnaissance in the desert frontier zone," The 
Geographical Journal, 115: 4—6. 

Goodman, D., Schneider, K., Piro, S., Nishimura, Y. and Pantel, A. (2007) "Ground penetrating 
radar advances in subsurface imaging for archaeology," in J. Wiseman and F. El-Baz (eds) Remote 
Sensing in Archaeology, Interdisciplinary Contributions to Archaeology book series, New York: 

Goossens, R. and Van Ranst, E. (1998) "The use of remote sensing to map gypsiferous soils in 
the Ismailia Province (Egypt)," Geoderma, 87: 47—56. 

Goossens, R., de Wulf, A., Bourgeois, J., Gheyle, W. and Willems, T. (2006) "Satellite imagery 
and archaeology: the example of Corona in the Altai Mountains," Journal of Archaeological 
Science, 33 (6): 745-55. 

Gore, P. (2004) "Archaeological site prediction: a multispectral approach," in C. Wang (ed.) Inter- 
national Conference on Remote Sensing Archaeology , Beijing: Chinese Center for Remote Sensing 

Goskar, T, Carty, A., Cripps, P., Brayne, C. and Vickers, D. (2003) "The Stonehenge Lasershow," 
British Archaeology, 73 (4): 9—15. 



Gould, R. (1987) "Archaeological survey by air: a case from the Australian Desert," Journal of 
Field Archaeology, 14 (4): 431-443. 

Green, N.E. (1957) "Aerial photographic interpretation and the social structure of the city," 
Photogrammetric Engineering, 23: 89—96. 

Gron, O., Aurdal, L., Christensen, F. and Loska, A. (2004) "Mapping and verifying invisible 
archaeological sites in agricultural fields by means of multi-spectral satellite images and soil 
chemistry," in C. Wang (ed.) International Conference on Remote Sensing Archaeology , Beijing: 
Chinese Center for Remote Sensing Archaeology. 

Gron, O., Christensen, F, Orlando, P., Baarstad, I. and MacPhail, R. (2006) "Hyperspectral and 
multispectral perspectives on the prehistoric cultural landscape: the ground- truthed chemical 
character of prehistoric settlement and infrastructure identified from space," in S. Campana 
and M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology, 
Oxford: British Archaeology Reports. 

Gumerman, G.J. and Lyons, T.R. (1971) "Archaeological methodology and remote sensing," 
Science, 172 (3979): 126-32. 

Gumerman, G.J. and Neely, J. A. (1972) "An archaeological survey of the Tehuacan Valley, 
Mexico: a test of color infrared photography," American Antiquity, 37 (4): 523-7. 

Gumerman, G.J. and Schaber, G. (1969) "Infrared scanning images: an archeological application," 
Science, 164 (3880): 712-13. 

Gunn, J.D., Folan, W.J. and Robichaux, H.R. (1995) "A landscape analysis of the Candelaria 
Watershed in Mexico: insights into paleoclimates affecting upland horticulture in the southern 
Yucatan Peninsula semi-karst," Geoarchaeology , 10 (1): 3—42. 

Gupta, R.D., Rai, G.K. and Bhaskar, C.B. (2004) "GIS based user interactive system for man- 
agement of archaeological data of Kaushambi site," in C. Wang (ed.) International Conference 
on Remote Sensing Archaeology, Beijing: Chinese Center for Remote Sensing Archaeology. 

Guy, M. (1993) "SPOT and archaeology," SPOT Magazine (June): 19-21. 

Hadjimitsis, D.G, Themistocleous, K., Ioannides, M. and Clayton, C. (2006) "The registration 
and monitoring of cultural heritage sites in the Cyprus landscape using GIS and satellite 
remote sensing," in M. Ioannides, D.B. Arnold, F Niccolucci and K. Mania (eds) Proceedings of 
CIP A/VAST /EG /EuroMed 2006: 3 7 th CIPA International Workshop dedicated on e-Documentation 
and Standardization in Cultural Heritage, Aire-la-Ville, Switzerland: Eurographics. 

Hafner, K. and Rai, S. (2005) "Governments tremble at Google's bird's-eye view," New York 
Times, 20 December. 

Hagerman, J.B. and Bennett, D.A. (2000) "Construction of digital elevation models for archae- 
ological applications," in K.L. Wescott and R.J. Brandon (eds) Practical Applications of GIS for 
Archaeologists: A Predictive Modeling Kit, London: Taylor & Francis. 

Hailey, T.I. (2005) "The powered parachute as an archaeological aerial reconnaissance vehicle," 
Archaeological Prospection, 12 (2): 69-78. 

Hakobyan, H. and Palmer, R. (2002) "Prospects for aerial survey in Armenia," in R.H. Brewley 
and W. Raczkowski (eds) Aerial Archaeology: Developing Future Practice, NATO Science book 
series, vol. 1, Amsterdam: IOS Press. 

Halkon, P. (2006) "Reconstructing an Iron Age and Roman Landscape- new research in Foul- 
ness Valley, East Yorkshire, England," in S. Campana and M. Forte (eds) From Space to 
Place: 2nd International Conference on Remote Sensing in Archaeology, Oxford: British Archaeology 

Hamilton, D. (2007) Remote Sensing in Archaeology: An Explicitly North American Perspective, by 
J.K. Johnson (ed.) Reviewed in: Archaeological Prospection, 14: 149-50. 

Hamlin, C.L. (1977) "Machine processing of LANDSAT data: an introduction for anthropologists 
and archaeologists," MASCA Newsletter, 13 (1-2): 1-11. 

Handwerk, B. (2006) Google Earth satellite maps boost armchair archaeology. Online. Available 
HTTP: <http://news.nationalgeographic.eom/news/2006/l 1/061 107-archaeology.html. 



Hardy, D.D. (1983) "An overview of remote sensing: remote sensing in peat and terrain resource 
surveys," Symposium of IPS Commission, Aberdeen, Scotland. 

Harp, E. (ed.) (197 '5) Photography in Archaeological Research, School of American Research Advanced 
Seminar Seroes, Albuquerque: University of New Mexico Press. 

Harp, E.J. (1966) "Anthropology and remote sensing," Bedford, Mass., Office of Aerospace 
Research, Air Force Cambridge Research Laboratories, Terrestrial Sciences Laboratory. 

Harrower, M., McCorriston, J. and Oches, E.A. (2002) "Mapping the roots of agriculture in 
southern Arabia: the application of satellite remote sensing, global positioning system, and 
geographic information system technologies," Archaeological Prospection, 9 (1): 35-42. 

Hassan, F.A. (1997) "The dynamics of a riverine civilization: a geoarchaeological perspective on 
the Nile Valley, Egypt," World Archaeology ; 29 (1): 51-74. 

Heimlich, R. (2004) "Archaologie aus dem All Satellitenbilder geben Hinweise auf antke 
Statten Vor neun Jahren gb US-Prasident Clinton Spionagebilder frei- eine Fundgrube fur die 
Agyptologen," Kolner Stadt-Anzeiger and Frankfurter Rundschau, Koln: Frankfurt. 

Helly, B., Bravard, J. and Caputo, R. (1992) "La plaine orientale de Thessalie (Grece): mobilite 
des paysages historiques et evolution tecto-sedimentaire," Actes du colloque dur revolution de 
pay sages historiques dans les pays Mediterraneens ; Colloque de Ravello, 35: 1-90. 

Herbich, T. (2003) "Archaeological geophysics in Egypt: the Polish contribution," Archaeological 
Polonia, 41: 13-56. 

Hererra, J. H., Trejo, D. and Covarrubias, M. (2002) "The creation of a GIS archaeological site 
catalogue in Yucatan, Mexico: a tool to preserve its cultural heritage," in B. Warmbein (ed.) 
Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 
Space Agency. 

Herodotus (2003) The Histories, London: Penguin Classics. 

Hijazi, J.H. and Qudah, O. (1997) "Use of SPOT satellite data for the mapping of floods in the 
archaeological site of Petra," Studies in the History and Archaeology of Jordan, 6: 51-5. 

Hirata, M., Koga, N., Shinjo, H., Fujita, H., Gintzburgery, G. and Mayiazak, A. (2001) 
"Vegetation classification by satellite image processing in a dry area of north-eastern Syria," 
International Journal for Remote Sensing, 22(4): 507-16. 

Hixson, D.R. (2005) "Measuring a Maya metropolis," Institute of Maya Studies Newsletter, 
34(1): 1-3. 

Hodder, I. and Orton, C. (1 976) Spatial Analysis in Archaeology, Cambridge: Cambridge University 

Hoffman, M. A. (1983) Remote Sensing: A Handbook for Archaeologists and Cultural Resource Managers, 
by T.R. Lyons and T.E. Avery; Remote Sensing: Practical Exercises on Remote Sensing in Archaeology. 
Supplement No. 1 , by T.E. Avery and T.R. Lyons; Remote Sensing: Instrumentation for N on- destructive 
Exploration of Cultural Resources. Supplement No. 2, by S.A. Morain and T.K. Budge; Remote 
Sensing: Aerial Anthropological Perspectives: A Bibliography of Remote Sensing in Cultural Resource 
Studies. Supplement No. 3, by T.R. Lyons, R.K. Hitchcock and W.H. Wills; Remote Sensing: 
A Handbook for Archaeologists and Cultural Resource Managers Basic Manual Supplement. Supplement 
No. 4, by M. Aikens, W.G. Loy, M.D. Southard and R.C. Hanes; Remote Sensing: Multispectral 
Analyses of Cultural Resources: Chaco Canyon and Bandelier national Monument. Supplement No. 5, 
by T.R. Lyons (ed.); Remote Sensing: Archaeological applications of Remote Sensing in the North 
Central Lowlands. Supplement No. 6, by C. Baker and G.J. Gumerman; and Remote Sensing: Aerial 
and Terrestrial Photography for Archaeologists. Supplement No. 7, by T.E. Avery and T.R. Lyons. 
Reviewed in: American Antiquity, 48 (1): 203—4. 

Hoffman, P. (2001) "Detecting informal settlements from Ikonos images using methods of object 
oriented image analysis — an example from Cape Town, South Africa," in C. Jurgens (ed.) Remote 
Sensing of Urban Areas, Regensberger: Geographische Schriften. 

Holcomb, D. (1992) "Shuttle imaging radar and archaeological survey in China's Taklamakan 
Desert , " Journal of Field Archaeology , 19: 12 9—3 8 . 



Holcomb, D. (2002) "Remote sensing and GIS technology for monitoring UNESCO World Her- 
itage Sites: a pilot project," in B. Warmbein (ed.) Proceedings of the Conference Space Applications 

for Heritage Conservation, Strasbourg: European Space Agency. 
Holcomb, D. and Shingiray, I.L. (2007) "Imaging radar in archaeological investigations: an 

image processing perspective," in J. Wiseman and E El-Baz (eds) Remote Sensing in Archaeology, 

Interdisciplinary Contributions to Archaeology book series, New York: Springer. 
Holden, N., Home, P. and Bewley, R. (2002) "High resolution digital airborne mapping and 

archaeology," in R.H. Brewley and W. Raczkowski (eds) Aerial Archaeology: Developing Future 

Practice, NATO Science book series, vol. 1, Amsterdam: IOS Press. 
Holladay, J.S. (1986) "The stables of ancient Israel: functional determinants of stable construction 

and the interpretation of pillared building remains of the Palestinian Iron Age," in L.T. Geraty 

and L.G. Hertt (eds) The Archaeology of Jordan and Other Studies Presented to Siegfried H. Horn. 

Berrien Springs, MI: Andrews University Press. 
Holley, G.R., Ritina A., Dalan, P. and Smith, A. (1993) "Investigations in the Cahokia site Grand 

Plaza," American Antiquity, 58 (2): 306-19- 
Hritz, C. and Wilkinson, T. (2006) "Using shuttle radar topography to map ancient water 

channels in Mesopotamia," Antiquity, 80: 415-24. 
Huadong, G. and Changlin, W. (2004) "Remote sensing archaeology in China: status and 

progress," in C. Wang (ed.) International Conference on Remote Sensing Archaeology , Beijing: 

Chinese Center for Remote Sensing Archaeology. 
Hunt, T. and Lipo, C. (2005) "Mapping prehistoric statue roads on Easter Island," Antiquity, 79: 

Hurcom, S. and Harrison, R. (1998) "The NDVI and spectral decomposition for semi-arid 

vegetation abundance estimation," International Journal for Remote Sensing 19(16): 3109-25. 
Hvistedahl, M. (2008, May 23) "The space archaeologists." Popular Science. Online. Available 

HTTP: <> (accessed 10 

September 2008). 
Ishwaran, N. and Stone, R.I. (2002) "Heritage learning and data collection: biodiversity and 

heritage conservation through collaborative monitoring and research," in B. Warmbein (ed.) 

Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg, France. 
James, J. (1995) "Shuttle radar maps ancient Angkor," Science, 267 (5200): 965. 
Jeffreys, D. (2003) "Introducing 200 years of ancient Egypt: modern history, ancient archaeology," 

in D. Jeffreys, (ed.) Views of Ancient Egypt since Napoleon Bonaparte: Imperialism, Colonialism, and 

Modern Appropriation, London: UCL Press. 
Jeffreys, D. and Tavares, A. (1994) "The historic landscape of Early Dynastic Memphis," 

Mitteilungen des Deutschen Archaologischen Instituts Abteilung Kairo, 50: 143-73. 
Jeffreys, D. and Tavares, A. (2001) "An integrated mapping project for the Saqqara plateau and 

escarpment," in M. Barta and J. Krejc' (eds) Abusir and Saqqara in the Year 2000. Archive 

Orientalni: Praha. 
Jennings, J. and Craig, N. (2003) "Using GIS for politywide analysis of Wari imperial political 

economy, "Journal of GIS in Archaeology, 1: 33-46. 
Jensen, J. (1996) Introductory Digital Image Processing: A Remote Sensing Perspective, Upper Saddle 

River: Prentice Hall. 
Jensen, J. (2000) Remote Sensing of the Environment: An Earth Resource Perspective, New York: Prentice 

Jingjing, Y.L. (2006) "Applications of remote sensing archaeology technologies in China," in 

S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing 

in Archaeology, Oxford: British Archaeology Reports. 
Johnson, A. (2005) Plane and Geodetic Surveying: The Management of Control Networks, London: 

Johnson, G.R. and Piatt, R.R. (1930) Peru from the Air, London: American Geographical Society. 



Johnson, J. (1950) "The Dura air photographs," Archaeology , 3: 158-9- 

Johnson, J. K. (1996) Delta Digitizing: GIS and Remote Sensing in Northwest Mississippi. New Methods, 

Old Problems: Geographic Information Systems in Modern Archaeological Research, Carbondale, IL: 

Center for Archaeological Investigations, Southern Illinois University. 
Johnson, J.K. (ed.) (2006) Remote Sensing in Archaeology: An Explicitly North American Perspective, 

Tuscaloosa: University of Alabama Press. 
Jomard, E.F. (1829) "Description des Antiquities d'Athribis, de Thumis, et de Plusiers 

nomes du Delta Oriental," in C.L.F. Panckoucke (ed.) Memoires de Description de VEgypte 

IX, Paris. 
Jorde, L.B. and Bertram, J. B. (1976) "Current and future applications of aerospace remote sensing 

in archaeology: a feasibility study," Reports of the Chaco Center, 1: 11-67. 
Joyce, C, Fuller, D. and Noel, M. (1992) "Archaeology takes to the skies," New Scientist, 133 

(1805): 42. 
Kamei, M. and Nakagoshi, N. (2002) "Assessing integrity in cultural landscape: a case study 

from Japan," in B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage 

Conservation, Strasbourg: European Space Agency. 
Kardulias, P.N. (2002) The Sydney Cyprus Survey Project: Social Approaches to Regional Archaeological 

Survey, by Michael Given and A. Bernard Knapp. Reviewed in: Journal of Field Archaeology, 

29 (3-4): 483-6. 
Karnieli, A., Gabai, A., Ichoku, C, Zaady, E. and Shachak, M. (2002) "Temporal dynamics of 

soil and vegetation spectral responses in a semi-arid environment," International Journal for 

Remote Sensing, 23 (19): 4073-87. 
Kelong, T, Yuqing, W., Qingbo, D., Xiaohu, Z., Dewen, W. and Xinlong, N. (2004) "Remote 

sensing archaeological research of the first emperor mausoleum in Qin Dynasty (Qinshihuang's 

Mausoleum)," in C. Wang (ed.) International Conference on Remote Sensing Archaeology , Beijing: 

Chinese Center for Remote Sensing Archaeology. 
Kelsey, F. (1927) "Les foules et les livres," Chronique D'Egypte, 3: 78-9- 
Kemp, B. (1972) "Temple and town in ancient Egypt," in P. Ucko, R. Tringham and 

G. W. Dimbleby (eds), Man, Settlement and Urbanism, London: Duckworth. 
Kemp, B. (2005) "Settlement and landscape in the Amarna area in the Late Roman Period," in 

J. Faiers (ed.) Late Roman Pottery at Amarna and Related Studies, London: Egypt Exploration 

Kemp, B. and Garfi, S. (1993) A Survey of the Ancient City of El-' Amarna, London: Egypt 

Exploration Society. 
Kennedy, A. (1925) Petra: Its History And Monuments , London: Country Life. 
Kennedy, D. (1995) "Water supply and use in the southern Hauran, Jordan," Journal of Field 

Archaeology, 22 (3): 275-90. 
Kennedy, D. (1997) "Roman roads and routes in northeast Jordan," Levant, 29: 71-93. 
Kennedy, D. (1998) "Declassified satellite photographs and archaeology in the Middle East: case 

studies from Turkey," Antiquity, 72: 553—61. 
Kennedy, D. (2002) "Aerial archaeology in the Middle East: the role of the military: past, present 

... and future?," in R.H. Brewley and W. Raczkowski (eds) Aerial Archaeology: Developing Future 

Practice, NATO Science book series, vol. 1, Amsterdam: IOS Press. 
Kennedy, D. and Bewley, R. (2004) Ancient Jordan from the Air, London: Council for British 

Research in the Levant. 
Kenyon, J.L. (1991) Air Photography and Archaeology, by D.N. Riley. Reviewed in: American 

Antiquity, 56 (1): 163-4. 
Kessler, D. (1981) Historische Topographie der Region zeischen Mallawi und Samalut. Beihefte zum 

Tubinger Atlas des Vorderen Orients 30, Wiesbaden: Wiesbaden Press. 
Khawaga, M. (1979) "A contribution to the fractal pattern of the Abu Tartar plateau: Western 

Desert, Egypt," Annals of the Geological Survey of Egypt, 9: 163—71. 



Kidder, A.V. (1929) "Air exploration of the Maya country," Bulletin of the Pan-American Union, 

63: 1200-5. 
Kidder, A.V. (1930) "Five days over Maya country," Scientific Monthly, 30: 193-205. 
Kidder, T. and Saucier, R. (1991) "Archaeological and geological evidence for proto-historic 

water management in northeast Louisiana," Geoarchaeology, 6 (4): 307-35. 
Kligman, D.M. (2006) "Teaching and using remote sensing in Argentine archaeology: evaluating 

the University of Buenos Aires curriculum and the graduation theses of the last decade," in 

S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing 

in Archaeology, Oxford: British Archaeology Reports. 
Knapp, A.B. (2000) Approaches to Landscape, by Richard Muir. Reviewed in: Journal of 

Anthropological Research, 56 (3): 420-2. 
Knisely-Marpole, R. (2001) "Kite aerial photography in Egypt's Western Desert," Aerial 

Archaeology Research Group News, 23: 33—7. 
Kouchoukos, N. (2001) "Satellite images and Near Eastern Landscapes," Near Eastern Archaeology , 

64(1-2): 80-91. 
Krishnamoorthy, R., Bharthi, G.S., Periakali, P. and Ramachandran, S. (2002) "Remote sensing 

and GIS applications for protection and conservation of World Heritage Site and ecosystems 

on the coastal zone of Tamil Nadu, India," in B. Warmbein (ed.) Proceedings of the Conference 

Space Applications for Heritage Conservation, Strasbourg: European Space Agency. 
Kruckman, L. (1987) "The role of remote sensing in ethnohistorical research, "Journal of Field 

Archaeology, 14 (3): 343-51. 
Komatsu, G, Olsen, J, Ormo, J, di Achille, G., Kring, D., Matsui, T. (2006) "The Tsenkher struc- 
ture in the Gobi-Altai, Mongolia: Geomorphological hints of an impact origin," Geomorphology, 

74, (1-4): 164-180. 
Kvamme, K.L. (2007) "Integrating multiple geophysical datasets," in J. Wiseman and F. El-Baz 

(eds) Remote Sensing in Archaeology, Interdisciplinary Contributions to Archaeology book series, 

New York: Springer. 
Kvamme, K.L. (2008) "Archaeological prospecting at the Double Ditch State Historic Site, 

North Dakota, USA," Archaeological Prospection, 15(1): 62-79- 
Ladefoged, T.N., McLachlan, S.M., Ross, S.C.L., Sheppard, P.J. and Sutton, D.G. (1995) "GIS- 

based image enhancement of conductivity and magnetic susceptibility data from Ureturituri 

Pa and Fort Resolution, New Zealand," American Antiquity, 60 (3): 471-81. 
Lambers, K., Eisenbaiss, H., Sauerbier, M., Kupferschmidt, D., Gaisecker, T, Sotoodeh, S. and 

Hanusch, T. (2007) "Combining photogrammetry and laser scanning for the recording and 

modeling of the Late Intermediate Period Site of Pinchango Alto, Palpa, Peru," Journal of 

Archaeological Science, 34(10): 1702—12. 
Lambert, D.P. (1997) GPS Satellite Surveying, by Alfred Leick. Reviewed in: Annals of the Association 

of American Geographers, 87 (2): 401-3. 
Lambin, E.F., Walkey, J. A. and Petit-Maire, N. (1995) "Detection of Holocene Lakes in 

the Sahara using satellite remote sensing," Photogrammetric Engineering and Remote Sensing, 

61 (6): 731-7. 
Landis, K. (2004) "Testing history: 200 years ago, Lewis and Clark mapped the West with crude 

tools and little technology. How close did they get to the truth? Even NASA wants to know," 

Boy's Life, 94: 30-4. 
Lasaponara, R. and Masini, N. (2006a) "Performance evaluation of data fusion algorithims for 

the detection of archaeological features by using satellite QuickBird data," in S. Campana and 

M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , 

Oxford: British Archaeology Reports. 
Lasaponara, R. and Masini, N. (2006b) "Identification of archaeological buried remains based on 

the normalized difference vegetation index (NDVI) from QuickBird satellite data," Geoscience 

and Remote Sensing Letters, 3 (3): 325—8. 



Lasaponara, R. and Masini, N. (2007) "Detection of archaeological crop marks by using satellite 

QuickBird multispectral imagery ," Journal of Archaeological Science, 34 (2): 214-21. 
Lawrence, W., Imhoff, M., Kerles, N. and Stutzer, D. (2002) "Quantifying urban land use and 

impact in Egypt using diurnal imagery of the earth's surface," International Journal for Remote 

Sensing, 23: 3921-37. 
Lemmens, J.P.M.M., Stancic, Z. and Verwaal, R.G. (1993) "Automated archaeological feature 

extraction from digital aerial photographs," in J. Andressen, T. Madsen and I. Scollar (eds) 

Computing the Past: Computer Applications and Quantitative Methods in Archaeology: Proceedings of 

the 20th CAA Conference Held at Aarhus University, Aarhus: Aarhus University Press. 
Lenney, M., Woodcock, C, Collins, J. and Hamdi, J. (1996) "The status of agricultural lands in 

Egypt: the use of multispectral NDVI features derived from Landsat TM," Remote Sensing of 

the Environment ; 56: 8-20. 
Lepsius, K. (1859) Denkmaler aus Aegypten und Aethiopien, Berlin: Nicolai. 
Leucci G. (2002) "Ground penetrating radar survey to map the location of buried structures 

under two churches," Archaeological Prospection, 9: 217-28. 
Leucci, G., Negri, S. and Ricchetti, E. (2002) "Integration of high resolution optical satel- 
lite imagery and geophysical survey for archaeological prospect ion in Hierapolis (Turkey)," 

Proceedings of the IGARSS 2002 Geoscience and Remote Sensing Symposium, 4: 1991-3. 
Li, X., Ruixia, Y. and Jian, X. (2004) "The remote sensing foundation research into the spatial 

information of ancient ruins and their numerically emulational conjectures," in C. Wang 

(ed.) International Conference on Remote Sensing Archaeology, Beijing: Chinese Center for Remote 

Sensing Archaeology. 
Lillesand, R., Kiefer, R. and Chipman, J. (200 4) Remote Sensing and 'Image Interpretation, New York: 

John Wiley and Sons. 
Limp, W. (1989) The Use of Multispectral Satellite Imagery in Archaeological Investigations, 

Fayetteville: Arkansas Archaeological Survey. 
Limp, W. (1992a) Applications of multispectral digital imagery in Southeastern archaeological 

investigations, in J. Johnson, (ed.), Method and Theory in Southeastern Archaeology , Tuscaloosa: 

University of Alabama Press. 
Limp, W. (1992b) The Use of Multispectral Digital Imagery in Archaeological Investigations , Arkansas 

Archaeological Survey Research Series book series, vol. 34, Fayetteville, Arkansas: Kansas 

Archaeological Survey. 
Limp, W. (2000) Anthropology, Space and Geographic Information Systems, by Mark Aldenderfer and 

Herbert D.G. Maschner. Reviewed in: Journal of Field Archaeology, 27 (2): 223—6. 
Lindbergh, C.A. (1929a) "Colonel and Mrs. Lindbergh aid archaeologists," in Carnegie Institute 

Reports, New York: Carnegie Institute. 
Lindbergh, C.A. (1929b) "The discovery of the ruined Maya cities," Science, 70, 12—13. 
Liu, L., Chen, X., Lee, Y.K., Wright, H. and Rosen, A. (2002) "Settlement patterns and devel- 
opment of social complexity in the Yiluo Region, North China, "Journal of Field Archaeology , 

29 (1-2): 75-100. 
Lorenza, H., Hernandez, M.C. and Cuellar, V. (2002) "Selected radar images of man-made 

underground galleries," Archaeological Prospection, 9: 1—7. 
Lunden, B. (1985) "Aerial thermography: a remote sensing technique applied to detection of 

buried archaeological remains at a site in Dalecarlia, Sweden," Geografiska Annaler Series A, 

Physical Geography , 67 (1—2): 161—6. 
Lynott, J. and Wylie, A. (eds) (1995) Ethics and Archaeology: Challenges for the 1990s. Washington, 

DC: Society for American Archaeology. 
Lyons, T.R., B.G. Pouls, and Hitchcock, R.K. (1972) "The Kin Bineola irrigation study: An 

experiment in the use of aerial remote sensing techniques in archaeology," Proceedings of the 

Third Annual Conference in Remote Sensing in Arid Lands. Tuscon: Office of Arid Land Studies, 

University of Arizona. 



MacDonald, R. (1995) "Corona: success for space reconnaissance, a look into the cold war, and a 

revolution for intelligence," Photogrammetric Engineering and Remote Sensing, 61(6): 689-720. 
Mc Adams, R. (1981) Heartland of Cities: Surveys of ancient settlement and land use on the central 

floodplain of the Euphrates, Chicago: University of Chicago Press. 
McCauley, J., Schaber, G., Breed, C, Grolier, M., Haynes, C, Issawi, B., Elachi, C. and Blom, R. 

(1982) "Subsurface valleys and geoarchaeology of the eastern Sahara revealed by shuttle radar," 

Science, 218: 100A-20. 
McGovern, P., Sever, T.L., Myers, J.W., Myers, E.E., Bevan, B., Miller, N.F., Bottema, S., 

Hongo, H., Meadow, R.H., Kuniholm, P.I., Bowman, S.G.E., Leese, M.N., Hedges, R.E.M., 

Matson, F.R., Freestone, I.C., Vaughan, S.J., Henderson, J., Vandiver, P.B., Thuesen, C.S. 

and Sease, C. (1995) "Science in archaeology: a review," American Journal of Archaeology, 

95 (1): 79-142. 
McHugh, W., Breed, C, Schaber, G., McCauley, J. and Szabo, B. (1988) "Acheulian sites along 

the 'Radar rivers,' southern Egyptian Sahara," Journal of 'Field Archaeology, 15: 361-79- 
McHugh, W., McCauley, J., Haynes, C, Breed, C. and Schaber, G. (1988) "Paleorivers and 

geoarchaeology in the southern Egyptian Sahara," Geoarchaeology, 3(1): 1—40. 
McHugh, W., Schaber, G., Breed, C. and McCauley, J. (1989) "Neolithic adaptation and the 

Holocene functioning of Tertiary palaeodrainages in southern Egypt and northern Sudan," 

Antiquity, 63: 320—36. 
McKinley, A.C. (1921) Photos of the Cahokia Mounds. Exploration and fieldwork of the Smithsonian 

Institution in 1921, Washington, DC: Smithsonian Institution. 
McManus, K., Donoghue, D., Brooke, C. and Marsh, S. (2002) "Airborne thermography of the 

vegetation-soil interface for detecting shallow ground disturbance," in B. Warmbein (ed.) 

Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 

Space Agency. 
Madry, S. and Crumley, C. (1990) "An application of remote sensing and GIS in a regional 

archaeological settlement pattern analysis: the Arroux River Valley, Burgundy, France," in 

K. Allen, S. Green and E. Zubrow (eds) Interpreting Space: GIS and Archaeology , Bristol, PA: 

Taylor and Francis. 
Mahanta, H.C. (1999) "Applications of satellite remote sensing in archaeological research," 

Bulletin of the Department of Anthropology, Dibrugarh University, 27: 46—60. 
Marcolongo, B. and Bonacossi, D.M. (1997) "L' abandon du systeme d'irrigation qatabanite dans 

la vallee du wadi Bayhan (Yemen): analyse geo-archeologique," Comptes Rendus de I'Academie 

des Sciences — Series II A — Earth and Planetary Science, 325 (1): 79—86. 
Marcolongo, B., Ninfo, A. and Simone, M. (2006) "'Valle d'Agredo': a paleoenvironmental 

and geoarchaeological reconstruction based on remote sensing analysis," in S. Campana and 

M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , 

Oxford: British Archaeology Reports. 
Martinez-Navarrete, M.I., J. Vincent-Garcia, P. Lopez-Garcia, J. Lopez-Saez, I Zavala-Morencos, 

and P. Diaz Del Rio, "Metallurgy at Kargaly and reconstruction of environment," Rossijskaa 

Arheologia, 4: 84-91. 
Martini, P.R. and Souza, I.M. (2002) "Satellite remote sensing as a tool to monitor Indian 

reservation in the Brazilian Amazonia," in B. Warmbein (ed.) Proceedings of the Conference Space 

Applications for Heritage Conservation, Strasbourg: European Space Agency. 
Masini, N. and Lasaponara, R. (2006a) "Evaluation of the spectral capability of QuickBird 

imagery for the detection of archaeological buried remains," in S. Campana and M. Forte (eds) 

From Space to Place: 2nd International Conference on Remote Sensing in Archaeology, Oxford: British 

Archaeology Reports. 
Masini, N. and Lasaponara, R. (2006b) "Investigating the spectral capability of QuickBird data 

to detect archaeological remains buried under vegetated and not vegetated areas," Journal of 

Cultural Heritage, 8 (1): 53—60. 



Masini, N. and Lasaponara, R. (2007) "Satellite-based recognition of landscape archaeologi- 
cal features related to ancient human transformation," Journal of Geophysics and Engineering, 

Master, S. and Woldai, T. (2004) The Umm Al-Binni Structure in the Mesopotamian Marshlands 
Of Southern Iraq, as a Postulated Late Holocene Meteorite Impact Crater: Geological Setting and 
New Landsat ETM+ and Aster Satellite Imagery, Johannesburg: University of Witwatersrand, 
Economic Geology Research Institute (EGRI). 

Mathys, T. (1997) "The use of declassified intelligence satellite photographs to map archaeological 
sites and surrounding landscape in the upper Syrian Jezira region," in International Symposium 
on Remote Sensing Applications in Archaeology ', Minneapolis: St. Cloud State University. 

Meats, C. (1996) "An appraisal of the problems involved in the three-dimensional ground 
penetrating radar imaging of archaeological features," Archaeometry, 38: 359-81. 

Meats, C. and Tite, M. (1995) "A ground penetrating radar survey at Rowbury Copse Banjo 
enclosure," Archaeological Prospection, 2(4): 229-236. 

Menze, B.H., Ur, J. A. and Sherratt, A.G. (2006) "Detection of ancient settlement mounds: 
archaeological survey based on the SRTM terrain model," Photogrammetric Engineering and 
Remote Sensing, 72: 321-7. 

Merola, P., Allegrini, A., Guglietta, D. and Sampieri, S. (2006) "Using vegetation indices to study 
archaeological areas," in S. Campana and M. Forte (eds) From Space to Place: 2nd International 
Conference on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 

Mindell, D. (2007) "Precision navigation and remote sensing for underwater archaeology," in 
J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology , Interdisciplinary Contributions 
to Archaeology book series, New York: Springer. 

Montufo, A. (1997) "The use of satellite imagery and digital image processing in land- 
scape archaeology, a case study from the Island of Mallorca, Spain," Geoarchaeology, 12: 

Moore E. and Freeman, A. (1997) "Radar, scattering mechanisms and the ancient landscape of 
Angkor," Remote Sensing Society Archaeology Special Interests Group Newsletter, 1: 15—19- 

Moore, E. (1989) "Water management in early Cambodia: evidence from aerial photography," 
The Geographical Journal, 155 (2): 204-14. 

Moore, E., Freeman, T. and Hensley, S. (1998) "Circular sites at Angkor: a radar scattering 
model," Journal of the Siam Society ', 85 (1-2): 107-19- 

Moore, E., Freeman, T. and Hensley, S. (2007) "Spaceborne and airborne radar at Angkor: intro- 
ducing new technology to the ancient site," in J. Wiseman and F. El-Baz (eds) Remote Sensing in 
Archaeology , Interdisciplinary Contributions to Archaeology book series, New York: Springer. 

Morozova, G.S. (2005) "A review of Holocene avulsions of the Tigris and Euphrates Rivers and 
possible effects on the evolution of civilizations in Lower Mesopotamia" Geoarchaeology , 20 (4): 

Moshier, S. and El-Kalani, A. (2008) "Late Bronze Age paleogeography along the ancient Ways 
of Horus in Northwest Sinai, Egypt," Geoarchaeology, 23 (4): 450—73. 

Moussa, A., Dolphin, L. and Mokhtar, G. (1977) Applications of Modern Sensing Technology to 
Egyptology, Menlo Park: SRI International. 

Mumford, G. (2002) "Reconstructing the ancient settlement at Tell Tebilla (East Delta)," Bulletin 
of the American Research Center in Egypt, 182: 18-23. 

Mumford, G. (2003) "Reconstruction of the temple at Tell Tebilla (East Delta)," in G. Knoppers 
and A. Hirsh (eds) Egypt, Israel and the Ancient Mediterranean World: Studies in Honour of Donald 
B. Redford, Leiden: Brill. 

Mumford, G. and Parcak, S. (2002) "Satellite imagery analysis and new fieldwork in South Sinai, 
Egypt (El-Markha Plain)," Antiquity, 76 (4): 953-4. 

Mumford, G. and Parcak, S. (2003) "Pharaonic ventures into South Sinai: El-Markha Plain Site 
346," Journal of Egyptian Archaeology, 89: 83—116. 



Mumford, G., Pavlish, L. and D'Andrea, C. (2003) "Geotechnical survey at Tell Tabilla, north- 
eastern Nile Delta, Egypt," in Z. Hawass (ed.) Egyptology at the Dawn of the Twenty-First 
Century: History, Religion: Proceedings of the Eighth International Congress of Egyptologists, Cairo, 
2000, Cairo: The American University in Cairo Press. 

Myers, J. W. (1989) "Science in archaeology: a review," American Journal of Archaeology, 93 (4): 599- 

NASA (2005) "NASA Worldwind forums> Worldwind v Google Earth Beta," in NASA (ed.) 
NASA Worldwind Forums, NASA. 

Nashef, K. (1990) "Archaeology in Iraq," American Journal of Archaeology ', 94 (2): 259-89- 

Nashef, K. (1992) "Archaeology in Iraq," American Journal of Archaeology ', 96 (2): 313-23. 

Niknami, K.A. (2004) "Application of remote sensing and geographic information (GIS) for 
the study of prehistoric archaeological site locations: case study from Garrangu River Basin, 
northwestern, Iran," in C. Wang (ed.) International Conference on Remote Sensing Archaeology, 
Beijing: Chinese Center for Remote Sensing Archaeology. 

Okayasu, T, Muto, M., Jamsran, U. and Takeuchi, K. (2007) "Spatially Heterogeneous Impacts 
on Rangeland after Social Systems Change in Mongolia," Land Degradation and Development, 
18(5): 555-66. 

Okin, G., Roberts, D., Murray, B. and Okin, W. (2001) "Practical limits on hyperspectral veg- 
etation discrimination in arid and semi-arid environments," Remote Sensing of the Environment, 
77 (2): 212-25. 

Orton, C. (2000) Sampling in Archaeology, Cambridge: Cambridge University Press. 

Ostir, K. and Nuninger, L. (2006) "Paleorelief detection and modeling: a case of study in eastern 
Laguedoc (France)," in S. Campana and M. Forte (eds) From Space to Place: 2nd International 
Conference on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 

Ostir, K., Kokalj, Z. and Sprajc, I. (2006) "Application of remote sensing in the detection of 
maya archaeological sites in South-Eastern Campeche, Mexico," in S. Campana and M. Forte 
(eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: 
British Archaeology Reports. 

Owen, G. (1993) "Looking down at Amarna," Aerial Archaeology Research Group News, 
6: 33-7. 

Palmer, R. (1978) "Computer transcriptions from air photographs: an explanation," Aerial 
Archaeology 2: 5—8. 

Palmer, R. (1993) "Remote sensing and archaeology," Aerial Archaeology Research Group News, 
7: 18-19. 

Palmer, R. (1995) "Integration of air photo interpretation and field survey projects," Archaeological 
Prospect ion, 2: 167-76. 

Palmer, R. (2002a) "A poor man's use of Corona images for archaeological survey," in Armenia 
in B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage Conservation, 
Strasbourg: European Space Agency. 

Palmer, R. (2002b) "Air photo interpretation and mapping to guide fieldwork in commer- 
cial archaeology in England," in R.H. Brewley and W. Raczkowski (eds) Aerial Archaeology: 
Developing Future Practice, NATO Science book series, vol. 1, Amsterdam: IOS Press. 

Parcak, S. (2003) "New methods for archaeological site detection via satellite image analysis: 
case studies from Sinai and the Delta," Archaeologia Polonia, 41: 243—5. 

Parcak, S. (2004a) "Egypt's Old Kingdom 'Empire'(P): A case study focusing on South Sinai," in 
A. Hirsch and G. Knoppers (eds) Egypt, Israel and the Ancient Mediterranean World: Studies in 
Honour of Donald B. Redford, Leiden: Brill. 

Parcak, S. (2004b) "Finding new archaeological sites in Egypt using satellite remote sensing: 
case studies from Middle Egypt and the Delta, in C. Wang (ed.) International Conference on 
Remote Sensing Archaeology , Beijing: Chinese Center for Remote Sensing Archaeology. 

Parcak, S. (2004c) "Satellite Remote Sensing Resources for Egyptology," Gottinger Mitzellen, 



Parcak, S. (2005) "Satellites and Survey in Middle Egypt," Egyptian Archaeology, 29: 28-32. 

Parcak, S. (2007a) "Going, going, gone: towards a satellite remote sensing methodology for mon- 
itoring archaeological tell sites under threat in the Middle East," Journal of Field Archaeology, 
42: 61-83. 

Parcak, S. (2007b) "The Middle Egypt Survey Project: 2005/6 season report," Journal of Egyptian 
Archaeology, 92: 3-8. 

Parcak, S. (2008) "Survey in Egyptology," in R. Wilkinson (ed.) Egyptology Today, Cambridge: 
Cambridge University Press. 

Parcak, S. (2009) "The skeptical remote senser," in S. Ikram and A. Dodson (eds) Beyond the 
Horizon: Studies in Egyptian Art, Archaeology and History in Honour of Barry J. Kemp, Cairo: 
American University in Cairo Press. 

Parker, A.G., Preston, G., Walkington, H. and Hodson, M.J. (2006) "Geomorphological and 
geoarchaeological studies from the lower gulf region of southeastern Arabia are increasing our 
knowledge of hydrological, ecological, and aeolian changes during the Holocene. Preliminary 
analyses of sediment records from two palaeolakes," Arabian Archaeology and Epigraphy , 17 (2): 

Parrington, M. (1983) "Remote Sensing," Annual Review of Anthropology , 12: 105-124. 

Parry, J. (1992) "The Investigative role of Landsat TM in the examination of pre- and 
proto-historic water management sites in Northeast Thailand," Geocarto International, 
4: 5-24. 

Parssinen, M., Salo, J. and Rasanen, M. (1996) "River floodplain relocations and the abandonment 
of aborigine settlements in the Upper Amazon Basin: a historical case study of San Miguel de 
Cunibos at the Middle Ucayalia River," Geoarchaeology, 11 (4): 345-59- 

Pavlish, L. (2004) "Archaeometry at Mendes, 1990-2002," in G. Knoppers and A. Hirsh 
(eds) Egypt, Israel and the Ancient Mediterranean World: Studies in Honour of Donald B. Redford, 
Leiden: Brill. 

Pavlish, L., Weeks, K. and D' Andrea, C. (2004) "Forthcoming Results of a magnetic survey over 
the remains of the Mortuary Temple of Amenophis I and surrounding environs," in H. Kars and 
E. Burke (eds) Proceedings of the 33rd International Symposium on Archaeometry, April 22-26, 2002, 
Geoarchaeological and bioarchaeological studies book series, vol. 3, Amsterdam: Institute for 
Geo- and Bioarchaeology, Vrije Universiteit. 

Peacock, D. (1993) "The site of Myos Hormos: a view from space," Journal of Roman Archaeology 
6: 226-32. 

Peebles, C.S. (2007) Review of Remote sensing in archaeology: an explicitly North American perspective, 
University of Alabama Press: Tuscaloosa, in Choice, AA (10): 1798. 

Peter, N. (2002) "Supporting environmental treaties with remote sensing data: an example of the 
application of a multilateral environmental agreement: The Kyoto Protocol," in B. Warmbein 
(ed.) Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 
Space Agency. 

Peterman, G.L. (1992) "Geographic Information Systems: Archaeology's Latest Tool," The Biblical 
Archaeologist, 55 (3): 162-7. 

Philip, G., Beck, A. and Donoghue, D. (2002) "The contribution of satellite imagery to archaeo- 
logical survey: an example from western Syria," in B. Warmbein (ed.) Proceedings of the Conference 
Space Applications for Heritage Conservation, Strasbourg: European Space Agency. 

Philip, G., Jabour, E, Beck, A., Bshesh, M., Kirk, A. and Grove, J. (2002) "Settlement and 
landscape development in the Horns Region, Syria: research questions, preliminary results 
1999-2000 and future potential," Levant, 34: 1-23. 

Piovan, S., Peretto, R. and Mozzi, P. (2006) "Palaeohydrogrpahy and ancient settlements in the 
Adige River Plain, between Rovigo and Adria (Italy)," in S. Campana and M. Forte (eds) 
From Space to Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: British 
Archaeology Reports. 



Piro, S. and Capanna, M.C. (2006) "Multimethodological approach to study the archaeological 
park of Maalga Karthago (Tunis) using remote sensing, archaeology, and geophysical prospect- 
ing methods," in S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference 
on Remote Sensing in Archaeology, Oxford: British Archaeology Reports. 

Poidebard, A. (1929) Les Revelations archeologiques de la Photographie aerienne-une nouvelle methode de 
Recherches et d' observations en region de Steppe, Paris: Editions Plon. 

Poidebard, A. (1931) Sur les traces de Rome-exploration archeologique aerienne en Syria, 
U illustration. 

Poidebard, A. (1934) La trace de Rome dans le desert de Syrie, Paris: Paul Geunther. 

Posnansky, M. (1982) "African archaeology comes of age," World Archaeology, 13 (3): 345—58. 

Pope, K. and Dahlin, B. (1989) "Ancient Maya wetland agriculture: new insights from ecological 
and remote sensing research," Journal of Field Archaeology, 16: 87-106. 

Pope, K.O., Dahlin, B.H. and Adams, R.E.W. (1993) "News and short contributions," Journal 
of Field Archaeology, 20 (3): 379-83. 

Pottier, C. (2002) "Mapping Angkor, for a new appraisal of the Angkor region," in B. Warmbein 
(ed.) Proceedings of the Conference Space Applications for Heritage Conservation, Strasbourg: European 
Space Agency. 

Pouls, B.G., Lyons, T.R. and Ebert, J. (1976) "Photogrammetric mapping and digitization of pre- 
historic architecture: techniques and applications in Chaco Canyon National Monument," in 
T.R. Lyons (ed.) Remote Sensing Experiments in Cultural Resource Studies: Non-Destructive Methods of 
Archaeological Exploration, Survey, and Analysis, Albuquerqe: National Park Service, University 
of New Mexico. 

Powesland, D. (2006) "Redefining past landscapes: 30 years of remote sensing in the Vale of 
Pickering," in S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference 
on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 

Powesland, D., Lyall J. and Donoghue, D. (1997) "Enhancing the record through remote 
sensing: the application and integration of multi-sensor, non-invasive remote sens- 
ing techniques for the enhancement of the sites and monuments record. Heslerton 
Parish Project, N. Yorkshire, England," Internet Archaeology , 2. Online. Available HTTP: 
<> (accessed 10 September 2008). 

Powesland, D., Lyall, J., Hopkinson, G., Donoghue, D., Beck, B., Harte, A. and Stott, D. 
(2006) "Beneath the sand-remote sensing, archaeology, aggregates, and sustainability: a case 
study from Heslerton, the Vale of Pickering, North Yorkshire, U.K," Archaeological Prospection, 
13 (4): 291-9. 

Pursell, C. (1991) "Preservation technologies: as answers get easier, questions remain hard," The 
Public Historian, 13 (3): 113-16. 

Qingjiu, T. and Jianqiu, H. (2004) "Hyperspectral Remote Sensing Archaeology for Shell Mound 
Site in Jiangsu," in C. Wang (ed.) International Conference on Remote Sensing Archaeology , Beijing: 
Chinese Center for Remote Sensing Archaeology. 

Rabeil, T, Mering, C. and Ramousse, R. (2002) "Using GIS and remote sensing in the man- 
agement of protected areas in West Africa; the example of the W National Park in Niger," 
in B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage Conservation, 
Strasbourg: European Space Agency. 

Rajamanickam, M., Chandrasekar, N. and Saravanan, S. (2004) "GIS— Based Shoreline Change 
Detection Between Kallar and Vembar Coast," in C. Wang (ed.) International Conference on 
Remote Sensing Archaeology , Beijing: Chinese Center for Remote Sensing Archaeology. 

Ramasamy, S.M., Venkatasubramanian, V, Abdullah, S.R. and Balaji, S. (1992) "The 
Phenomenon of River Migration in Northern Tamil Nadu: Evidence from Satellite Data, 
Archaeology, and Tamil Literature," Man and Environment , 7 (1): 13 — 26. 

Rapp, G. and Hill, C. (1998) Geoarchaeology: The Earth-Science Approach to Archaeological 
Interpretation, New Haven: Yale University Press. 



Rapp, Jr., G. (1975) "The geologist" Journal of Field Archaeology, 2 (3): 229—37. 

Rawson, J. (1996) "Sculpture for tombs and temples," in J. Rawson (ed.) The British Museum Book 
of Chinese Art, New York: Thames and Hudson. 

Rees, L.W.B. (1929) "The Transjordan Desert," Antiquity, 3: 389-407. 

Reindel, M., Cuadrado, J. and Lambers, K. (2006) "Altares en el desierto: Las estructuras de 
piedra sobre los geoglifos Nasca en Palpa". Arqueologia y Sociedad, 17, 179-222. 

Renfrew, C. and Bahn, P. (2000) Archaeology: Theories, Method, and Practice, New York: Thames 
and Hudson. 

Renfrew, C., Dixon, J. and Cann, J. (1966) "Obsidian and Early Cultural Contact in the Near 
East," Proceedings of the Prehistoric Society, 32: 30—72. 

Richardson, B. and Hritz, C. (2007) "Remote sensing and GIS use in the archaeological 
analysis of the central Mesopotamian Plain," in J. Wiseman and F. El-Baz (eds) Remote Sens- 
ing in Archaeology , Interdisciplinary Contributions to Archaeology book series, New York: 

Riccetti, E. (2001) "Remotely Sensed and Geophysical Data for Nondestructive Archaeologi- 
cal Prospection," Proceedings of the IGARSS 2001 Geoscience and Remote Sensing Symposium, 7: 

Riccetti, E. (2004) "Application of Optical High Resolution Satellite Imagery for Archaeological 
Prospection over Hieropolis (Turkey)," Proceedings of the IGARSS 2004 Geoscience and Remote 
Sensing Symposium, 6: 3898-901. 

Riley, D. (1982) Aerial Archaeology in Britain, Princes Risborough, Buckinghamshire: Shire. 

Riley, D. (1992) "Aerial Photography in Israel," Current Archaeology, 136: 139—142. 

Risbol, O., Gjersten, A.K. and Skare, K. (2006) "Airborne laser scanning of cultural remains 
in forests: some preliminary results from a Norwegian project," in S. Campana and M. Forte 
(eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology, Oxford: 
British Archaeology Reports. 

Rizzo, E., Chianese, D., Lapenna, V. and Piscitelli, S. (2003) Integration of Magnetometric, GPR, 
and Geoelectric Measurements Applied to the Study of the New Viggiano Archaeological Site 
(Southern Italy), Geophysical Research Abstracts, 5 : 02111. 

Roberts, A. (1990) An Analysis of Classic Lowland Maya Burials, by W.B.M. Welsh. Reviewed 
in: American Antiquity, 55 (2): 440—1. 

Robinson, C. (2002) "Application of satellite radar data suggests that the Kharga Depression in 
southwestern Egypt is a fracture rock aquifer," International Journal of Remote Sensing, 23 (19): 

Romano, D.D and Schoenbrun, B.C. (1993) "A Computerized Architectural and Topographical 
Survey of Ancient Corinth," Journal of Field Archaeology, 20 (2): 177—190. 

Roosevelt, A.C. (2007) "Geophysical Archaeology in the Lower Amazon: A Research Strategy," 
in J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology , Interdisciplinary Contributions 
to Archaeology book series, New York: Springer. 

Rossi, C and Ikram S. (2002) "Surveying the North Kharga Oasis," KMT, 13 (4): 72-9. 

Roughley, C. (2001) "Understanding the Neolithic landscape of the Carnac region: a GIS 
approach," in Z. Stancic and T. Veljanovski (eds), Proceedings of the Computer Applications 
and Quantitative Methods in Archaeology Conference, BAR International Series 931, Oxford, 

Roughley, C. (2002) "Les monuments de Bougon, partie integrante du paysage," in J. P. Mohen 
and C. Scarre (eds), Les Tumulus de Bougon: Complexe Megalithique du Ve au Hie Millenaire, Paris: 

Roughley, C. (2004) "Views of Carnac: applications of visibility analysis and dynamic visualisa- 
tion for understanding the Neolithic monuments of Southern Brittany," Internet Archaeology, 
16. Online. Available HTTP: <> 
(accessed 8 September 2008). 



Roughley, C, Sherratt, A. and Shell, C.A. (2002) "Past records new views: Carnac 1830-2000," 

Antiquity, 76 (291): 218-23; reprinted in T.C. Darvill and C. Malone (eds) (2003) Megaliths 

from Antiquity, Colchester: Antiquity Publications. 
Rowlands, A. and Sarris, A. (2007) "Detection of exposed and subsurface archaeological remains 

using multi-sensor remote sensing," Journal of Archaeological Science, 34 (5): 795-803. 
Rowlands, A., Sarris, A. and Bell, J. (2006) "Airborne multi-sensor remote sensing of exposed 

and subsurface archaeological remains at Itanos and Roussolakkos, Crete," in S. Campana and 

M. Forte (eds) From Space to Place: 2nd International Conference on Remote Sensing in Archaeology ■, 

Oxford: British Archaeology Reports. 
Said, R. (1988) Geological Evolution of the Nile Valley, New York: Springer. 
Said, R. (1990) The River Nile: Geology, Hydrology, and Utilization, New York: Pergamon Press. 
Salem, B., El-Cibahy, A. and El-Raey, M. (1995) "Change detection of land cover classes in 

agro-ecosystems of northern Egypt by remote sensing," International Journal of Remote Sensing, 

16(14): 2581-94. 
Sarris, A. (2005) "Use of remote sensing for archaeology: state of the art," Proceedings of the 

International Conference on the Use of Space Technologies for the Conservation of Natural and Cultural 

Heritage, Campeche, Mexico. 
Sarris, A. and Jones, R. (2000) "Geophysical and related techniques applied to archaeological 

survey in the Mediterranean: a review," Journal of Mediterranean Archaeology , 13 (1): 3—75. 
Sarris, A., Dunn, R.K., Rife, J. L., Papadopoulos, N., Kokkinou, E. and Mundigler, C. (2007) 

"Geological and geophysical investigations in the Roman cemetery at Kenchreai (Korinthia), 

Greece , " A rchaeological Prospect ion , 14(1): 1—23. 
Sarris, A., Topouzi, S., Chatziiordanou, E., Liu, J. and Xu, L. (2002) "Space technologies in 

archaeological research and CRM of semi-arid and desertification affected regions: examples 

from China and Greece," in B. Warmbein (ed.) Proceedings of the Conference Space Applications for 

Heritage Conservation, Strasbourg: European Space Agency. 
Sarris, A., Vafidis A., Mertikas S., Guy M., Vrontaki E., Manakou M. and Kalpaxis T. (1998) 

"Ancient Itanos (Erimoupolis, Lasithi): an archaeological site as a remote sensing laboratory," 

Proceedings of the 31st International Symposium of Archaeometry, 1998, Budapest, Hungary, Oxford: 

British Archaeological Reports. 
Sarris, A., Weymouth J., Cullen B., Stein C. and Wiseman J.T. (1996) "The Nikopolis Project - 

integration of geophysical prospection, satellite remote sensing, and GIS techniques in the 

study of Epirus, Greece," Proceedings of the 30th International Symposium of Archaeometry, Urbana, 

Saturno, W., Sever, T., Irwin, D., Howell, B. and Garrison, T. (2007), "Putting us on the map: 

remote sensing investigation of the ancient Maya landscape," in J. Wiseman and F. El-Baz 

(eds) Remote Sensing in Archaeology, Interdisciplinary Contributions to Archaeology book series, 

New York: Springer. 
Saturno, W., Stuart, D. and Beltran, B. (2006) "Early Maya writing at San Bartolo, Guatemala," 

Science, 511 6765): 1281-5. 
Schaedel, R. P. (1951) "The lost cities of Peru," Scientific American, 185(2): 18-24. 
Schiffer, M. (1987) Site Formation Processes of the Archaeological Record, Albuquerque: University of 

New Mexico Press. 
Schipper, F. (2005) "The protection and preservation of Iraq's archaeological heritage, Spring 

1991-2003 ," American Journal of Archaeology, 109(2): 251-72. 
Schmidt, A. (2001) Geophysical Data in Archaeology: A Guide to Good Practice, London, Oxbow 

Schmidt, H. and Karnieli, A. (2002) "Analysis of the temporal and spatial vegetation pat- 
terns in a semi-arid environment observed by NOAA AVHRR imagery and spectral ground 

measurements," International Journal for Remote Sensing, 23 (19): 3971-90. 
Scollar, I. (1963) "International colloquium on air archaeology," Antiquity, 37: 296—7. 



Scollar, I. (2002) "Making things look vertical," in R.H. Brewley and W. Raczkowski (eds) 
Aerial Archaeology: Developing Future Practice, NATO Science book series, vol. 1, Amsterdam: 
IOS Press. 

Scollar, I., Tabbagh, A., Hesse, A. and Herzog, I. (1990) Archaeological Prospecting and Remote 
Sensing: Topics in Remote Sensing, Cambridge, Cambridge University Press. 

Sever, T. (1995) "Remote sensing," American Journal of Archaeology, 99: 83-4. 

Sever, T. (1998) "Validating prehistoric and current social phenomena upon the landscape of the 
Peten, Guatemala," in D. Liverman, E. Moran, R. Rindfuss and P. Stern (eds) People and Pixels: 
Linking Remote Sensing and Social Science, Washington, DC: National Academy Press. 

Sever, T. and Wagner, D. (1991) "Analysis of prehistoric roadways in Chaco Canyon using 
remotely sense digital data," in C. Trombold (ed.) Ancient Road Networks and Settlement 
Hierarchies in the New World, Cambridge, Cambridge University Press. 

Sever, T. and Wiseman, J. (1985) Remote sensing in Archaeology: Potential for the Future, Report on a 
Conference, March 1—2, 1984, Earth Resources Laboratory, NSTL, Mississippi. 

Sever, T.L. and Irwin, D.E., (2003) "Remote-sensing investigation of the ancient Maya in the 
Peten rainforest of northern Guatemala," Ancient Mesoamerica, 14: 113—22. 

Sheets, P. (2004) The Origins of Monumentality in Ancient Costa Rica, Revealed by Satellite and 
Aircraft Remote Sensing (Invited Paper), in C. Wang (ed.) International Conference on Remote 
Sensing Archaeology, Beijing: Chinese Center for Remote Sensing Archaeology. 

Sheets, P. and Sever, T. (1988) "High tech wizardry," Archaeology, 41: 28-35. 

Sheets, P. and Sever, T. (1991) "Prehistoric footpaths in Costa Rica: transportation and com- 
munication in a tropical rainforest," in C. Trombold (ed.) Ancient road networks and settlement 
hierarchies in the New World, Cambridge, Cambridge University Press. 

Sheets, P. and Sever, T. (2006) "3-D Visualization of Ancient Professional Landscapes in Costa 
Rica," in S. Campana and M. Forte (eds) From Space to Place: 2nd International Conference on 
Remote Sensing in Archaeology, Oxford: British Archaeology Reports. 

Sheets, P. and Sever, T. (2007) "Creating and perpetuating social memory across the ancient 
Costa Rican landscape," in J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology, 
Interdisciplinary Contributions to Archaeology book series, New York: Springer. 

Sheets, P., Hoopes, J., Melson, W, McKee, B., Sever, T, Mueller, M., Chenault, M. and 
Bradley, J. (1991) "Prehistory and volcanism in the Arenal Area, Costa Rica," Journal of 
Field Archaeology, 18: 445-65. 

Shell, C. (2002) "Airborne high-resolution digital, visible, infra-red, and thermal sensing for 
archaeology," in R.H. Bewley and W. Raczkowski (eds) Aerial Archaeology: Developing Future 
Practice, NATO Science book series, vol. 1, Amsterdam: IOS Press. 

Shell, C.A. and Roughley, C.F. (2004) "Exploring the Loughcrew landscape: a new approach with 
airborne LIDAR," Archaeology Ireland, 18 (2): 20-3. 

Sherratt, A.G. (2004) "Spotting tells from space," Antiquity, 78: 301. 

Showater, P. (1993) "A thematic mapper analysis of the prehistoric Hohokam canal system, 
Phoenix, Arizona," Journal of Field Archaeology, 20: 77—90. 

Shupeng, C. (2004) "Environmental remote sensing monitoring and management information 
system using for protection of cultural heritage," invited paper in C. Wang (ed.) Interna- 
tional Conference on Remote Sensing Archaeology, Beijing: Chinese Center for Remote Sensing 

Shuren, L, Xianhua, L., Xinyuan, W., Meng, Y, Zhiming, Z. and Kelong, T. (2004) "Proposal on 
the task of remote sensing archaeology in China," in C. Wang (ed.) Proceedings of the International 
Conference for Remote Sensing Archaeology , Beijing: Center for Remote Sensing Archaeology. 

Sintubin, M., Phillipe., M., Similox-Tohon, D., Verhaert, G., Paulissen, E. and Waelkens, M. 
(2003) "Seismic catastrophes at the ancient city of Sagalassos (SW Turkey) and their 
implications for seismotectonics in the Burdur-Isparta area," Geological Journal, 38 (3-4): 



Sittler, B. and Schellberg, S. (2006) "The potential of LIDAR in assesing elements of cultural 

heritage hidden under forest canopies or overgrown by vegetation: possiblities and limits in 

detecting microrelief structures for archaeological surveys," in S. Campana and M. Forte (eds) 

From Space to Place: 2nd International Conference on Remote Sensing in Archaeology, Oxford: British 

Archaeology Reports. 
Snow, E. (1979) Remote Sensing Experiments in Cultural Resource Studies: Non-Destructive Methods of 

Archaeological Exploration, Survey, and Analysis, by Thomas R. Lyons; and Aerial Remote Sensing 

Techniques in Archaeology \ by Thomas R. Lyons and Robert K. Hitchcock. Reviewed in: Bulletin 

for the Association of Preservation Technology, 11 (4): 128-9- 
Soetens, S., Sarris, A. and Vansteenhuyse, K. (2002) "Defining the Minoan cultural landscape by 

the use of GIS," in B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage 

Conservation, Strasbourg: European Space Agency. 
Soghor, C. (1967) "The excavations at Tell el-Rub'a,"y^r/W of the American Research Center in 

Egypt, 6: 5-16. 
Som, N. (2002) "Remote sensing and GIS technology for identification of conservation and 

heritage sites in urban planning," in B. Warmbein (ed.) Proceedings of the Conference Space 

Applications for Heritage Conservation, Strasbourg: European Space Agency. 
Sonneman, T, Sauerbier, M., Remondino, F. and Schrotter, G. (2006) "Reality-based 3D mod- 
eling of the Angkorian temples using aerial imagery," in S. Campana and M. Forte (eds) 

From Space to Place: 2nd International Conference on Remote Sensing in Archaeology, Oxford: British 

Archaeology Reports. 
Soreide, F. (2000) "Cost effective deep water archaeology: preliminary investigations 

in Trondheim Harbour," The International Journal of Nautical Archaeology , 29 (2): 

Spencer, J. (1982) Excavations at El-Ashmunein, I: The Topography of the Site, London: British 

Museum Press. 
Spencer, J. (1989) Excavations at El-Ashmunein, II: The Temple Area, London: British Museum 

Spencer, J. (1993) Excavations at El-Ashmunein, 111: The Town, London: British Museum Press. 
St. Joseph, J.K.S. (ed.) (1966) The Uses of Air Photography, London: John Baker. 
Stafford, C, Leigh, D. and Asch, D. (1992) "Prehistoric settlement and landscape change on 

alluvial fans in the Upper Mississippi River Valley," Geoarchaeology 7 (4): 287-314. 
Stanley, J. D. and Jorstad, T.F. (2006) "Short contribution: buried canopic channel identified near 

Egypt's Nile Delta coast with radar (SRTM) imagery," Geoarchaeology 21 (5): 503—14. 
Stargardt, J. (2004) "Reconstructing the ancient landscape of peninsular Thailand," in C. Wang 

(ed.) International Conference on Remote Sensing Archaeology, Beijing: Chinese Center for Remote 

Sensing Archaeology. 
Steadman, S. (2000) "Spatial patterning and social complexity on prehistoric Anatolian tell sites: 

models for mounds," Journal for Anthropological Archaeology, 19(2): 164—99- 
Stein, A. (1919) "Air photography of ancient sites," The Geographical Journal, 54: 200. 
Stein, A. (1940) "Surveys on the Roman frontier in Iraq and Transjordan," The Geographical 

Journal, 95 (6): 428-38. 
Stein, C. and Cullen, B. (1994) "Satellite imagery and archaeology: a case study from Nikopolis," 

American Journal of Archaeology, 98 (2): 316. 
Steiner, C.B. (1987) Review of M. Banta and C. Hinsley, "From Site to Sight: Anthropology", 

Photography, and the Power of Imagery, Harvard University Press: Boston. In African Arts, 20 

(3): 81-2. 
Stern, R. and Salam, M.A. (1996) "The origin of the great Nile bend from Sir-C/X-Sar imagery," 

Science, 274(5293): 1696-8. 
Stewart, D. (2001) "New tricks with old maps: urban landscape change, GIS, and historic 

preservation in the less developed world," The Professional Geographer, 53 (3): 361—73. 



Stichelbaut, B. (2006) "The application of First World War aerial photography to archaeology: 

the Belgian images," Antiquity, 80 (307): 161-72. 
Stine, R. and Decker, T. (1990) "Archaeology, data integration and GIS," in K. Allen, S. Green 

and E. Zubrow, Interpreting Space: GIS and Archaeology, Bristol, PA: Taylor and Francis. 
Stone, E. (2008) "Patterns of looting in southern Iraq," Antiquity, 82 (315): 125-38. 
Stubbs, J. and McKees, K. (2007) "Applications of remote sensing to the understanding and 

management of cultural heritage sites," in J. Wiseman and F. El-Baz (eds) Remote Sensing in 

Archaeology ', Interdisciplinary Contributions to Archaeology book series, New York: Springer. 
Sultan, M., Fiske, M., Stein, T, Gamal, M., Abdel-Hady, Y, El-Araby, H., Madani, A., 

Mehanee, S. and Becker, R. (1999) "Satellite based monitoring of urbanization in the Nile 

Delta, Egypt," Ambio, 28: 628-31. 
Summers, G. (1992) "An aerial survey of Cevre Kale, Yarasli," Anatolian Studies, 42: 

Supajanya, T. (1989) "Remote sensing application in archaeological study in Tuny Kula Roghai, 

Northeast Thailand," Proceedings of Franco-Thai Workshop on Remote Sensing Nov. 2-4, 1989, 

Khon Kaen. 
Sykes, L. (1997) "Cities on the Silk Road," Geographical Magazine, 69: 23-6. 
Symanzik, J., Cook, D., Lewin, N., Ma jure, J.J. and Megretskaia, I. (2000) "Linking Arc View 

and XGobi: insight behind the front end," Journal of Computational and Graphical Statistics, 

9 (3): 470-90. 
Tan, K., Wan, Y, Zhou, X., Song, D. and Quan, D. (2006) "The application of remote sens- 
ing technology in the archaeological study of the Mausoleum of Emperor Qinshihuang," 

International Journal of Remote Sensing, 27 (16): 3347-63. 
Tartaglia, L.J. (1973) "Infrared photography: an application of remote sensing to archaeology," 

Anthropology U.C.L.A, 5 (2): 1-95. 
Tartaron, T.F. (2003) "The archaeological survey: sampling strategies and field methods," Hesperia 

Supplements, 32: 23-46. 
Taylor, P. (2007) "High tech archaeology supports quest for Noah's Ark: can high resolution 

satellite imagery certify one of ancient history's most coveted prizes?" Online. Available HTTP: 

<http://www.ei journal. com/Noah 's_Ark.asp> (accessed 8 September 2008). 
Thakker, P.S. (2002) "Archaeology through space: experience in Indian subcontinent," in 

B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage Conservation, 

Strasbourg: European Space Agency. 
Thakran, R.C. (2000) "Implications of partition on protohistoric investigations in the Ghaggar- 

Ganga Basins," Social Scientist, 28 (1-2): 42-67. 
Thompson, H. (1967) "A new development in archaeological air photography," Antiquity, 

41: 225-7. 
Tieszen, L.L., Reed, B.C., Bliss, N.B., Wylie, B.K. and Dejong, D.D. (1997) "NDVI, C3, and 

C4 production, and distributions in the Great Plains grassland land cover classes," Ecological 

Applications, 7 (1): 59-78. 
Timor, G. (2004) "Space and GIS technology in paleoenvironmental analysis (old maps, satellite 

images, and digital elevation models in archaeology)," Antaeus, 27: 135—44. 
Toprak, V. (2002) "Surface processes: obstacles in aerial archaeology, examples from Turkey," in 

R.H. Brewley and W. Raczkowski (eds) Aerial Archaeology: Developing Future Practice, NATO 

Science book series, vol. 1, Amsterdam: IOS Press. 
Traviglia, A. (2006) "Archaeological usability of hyperspectral images: sucesses and failures of 

image processing techniques," in S. Campana and M. Forte (eds) From Space to Place: 2nd 

International Conference on Remote Sensing in Archaeology , Oxford: British Archaeology Reports. 
Trelogan, J., Crawford, M., Teng, L., Kwon, O. and Carter, J. (1999) "Mapping the features of 

the Chora of Chersonesos via remotely sensed data," Proceedings of the IGARSS 1999 Geoscience 

and Remote Sensing Symposium, 5:25 69—7 1 . 



Trelogan, J.M.C. and Carter, J. (2002) "Monitoring the ancient countryside: remote sensing 
and GIS at the Chora of Chersonesos (Crimea, Ukraine)," in B. Warmbein (ed.) Proceed- 
ings of the Conference Space Applications for Heritage Conservation, Strasbourg: European Space 

Tykot, R.H. (1994) New Developments in Archaeological Science. A Joint Symposium of the Royal Society 
and the British Academy, February 1991, by A. M. Pollard (ed.). Reviewed in: American Journal 
of Archaeology, 98 (4): 774-6. 

Ur, J. (2003) "Corona satellite photography ancient road networks: a northern Mesopotamian 
case study," Antiquity, 11: 102-15. 

Ur, J. (2005) "Sennacherib's northern Assyrian canals: new insights from satellite imagery and 
aerial photography," Iraq, 61: 317-45. 

Ur, J. (2006) "Google Earth and archaeology," The SAA Archaeological Record, 6 (3): 35-8. 

Urwin, N. and Ireland, T. (1992) "Satellite imagery and landscape archaeology: interim report on 
the environmental component of the Vinhais Landscape Archaeology Project, North Portugal," 
Mediterranean Archaeology, 5 : 121-31. 

Van Andel, T.H. (1998) "Paleosols, red sediments, and the Old Stone Age in Greece," 
Geoarchaeology, 13 (4): 361-90. 

Van den Brink, E. (1987) "A geo-archaeological survey in the East Delta, Egypt: the first two 
seasons, a preliminary report," Mitteilungen des Deutschen Archaologischen Instituts Abteilung Kairo, 

Vercoutter, J. (1976) Mirgissa I, Paris: Centre National de la Recherche Scientifique. 

Verhoven, G. and Loenders, J. (2006) "Looking through black-tinted glasses— a remotely con- 
trolled infrared eye in the sky," in S. Campana and M. Forte (eds) From Space to Place: 2nd 
International Conference on Remote Sensing in Archaeology, Oxford: British Archaeology Reports. 

Vermeulen, F. (1998) "A Computer-aided Geo-archaeological Survey of the Classical Landscape 
of Central Anatolia," in J. A Barcelo, J.A. et al (eds), New Techniques for Old Times CAA 98, 
Computer Applications and Quantitative Methods in Archaeology, Proceedings of the 26th Conference, 
Barcelona, March 1998, BAR-International Series: Oxford. 

Vicent, J., Ormeno, S., Martinez-Navarete, M. and Delgado, J. (2006) "The Kargaly Project: 
modelling Bronze Age landscapes in the Steppe," in S. Campana and M. Forte (eds) From Space to 
Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: British Archaeology 

Vining, B.R. and Wiseman, J. (2006) "Multispectral and synthetic aperture radar remote-sensing- 
based models for Holocene coastline development in the Ambracian Gulf, Epirus, Greece," 
Archaeological Prospection, 13 (4): 258-68. 

Wang, C. and Huadong, G. (2004) Proceedings of the International Conference for Remote Sensing 
Archaeology , Beijing: Chinese Center for Remote Sensing Archaeology. 

Watson, R.A. (1976) "Inference in archaeology," American Antiquity, 41 (1): 58-66. 

Weiss, H. (1991) "Archaeology in Syria," American Journal of Archaeology, 95 (4): 683—740. 

Weller, E.T. (2006) "Satellites, survey, and settlement: the late classic Maya utilization of bajos 
(seasonal swamps) at Tikal and Yaxha, Guatemala," in S. Campana and M. Forte (eds) From 
Space to Place: 2nd International Conference on Remote Sensing in Archaeology , Oxford: British 
Archaeology Reports. 

Welzenbach, M. (1995) "Search for the lost city," Reader's Digest, October: 84-89- 

Wendorf, E, Close, A. and Schild, R. (1987) "A survey of the Egyptian radar channels: an example 
of applied field archaeology," Journal of Field Archaeology, 14: 43—63. 

Wenlie, Z (1998) The Qin Terracotta Army, London: Scala Books. 

Wiegend, T. (1920) Wissenschaftliche Verbffentlichungen des deutsch-tilrkischen Denkmalschutz- 
Kommandos 1 , Berlin: de Gruyter. 

Wilkinson, K. , Beck, A.R. and Philip, G. (2006) "Satellite imagery as a resource in the prospection 
for archaeological sites in central Syria," Geoarchaeology, 21 (7): 735—50. 



Wilkinson, T.J. (1989) "Extensive sherd scatters and land-use intensity: some recent results," 

Journal of Field A rchaeology , 16(1): 31-46. 
Wilkinson, T.J. (2001) "Surface collection, field walking, theory and practice, sampling theories," 

in A. Pollard and D. Brothwell (eds) Handbook of Archaeological Sciences, New York: John Wiley. 
Wilkinson, T.J. (2002) "Archaeological survey of Tell Beydar region, Syria 1997," in 

K. Van Lerberghe and G. Voet (eds) Tell Beydar Environmental and Technical Studies, Begijnhof: 

Wilkinson, T.J. (2003) Archaeological Landscapes of the Near East, Tuscon: University of Arizona 

Wilkinson, T.J., Bintliff, J., Curvers, H.H., Halstead, P., Kohl, P.L., Liverani, M., McCorriston, J., 

Oates, J., Scwartz, G.M., Thuesen, I., Weiss, H. and Courty, M. (1994) "The structure and 

dynamics of dry-farming states in Upper Mesopotamia [and comments and reply}," Current 

Anthropology, 35 (5): 483-520. 
Wilkinson, T.J., Ur, J. and Casana, J. (2004) "Nucleation to dispersal: trends in settlement 

patterns in the northern Fertile Crescent," in S. Alcock and J. Cherry (eds) Side-by -Side Survey: 

Comparative Regional Studies in the Mediterranean World, Oxford: Oxbow. 
Wilkinson, T.J., Wilkinson, E., Ur, J. A. and Altaweel, A. (2005) "Landscape and settlement in 

the Neo- Assyrian Empire," Bulletin of the American Schools of Oriental Research, 340: 23-56. 
Williams-Hunt, P.D.R. (1950) "Irregular earthworks in eastern Siam: an air survey," Antiquity, 

24: 30-36. 
Willey, G.R. (1990) "Recent Advances in Maya Archaeology," Bulletin of the American Academy 

of Arts and Sciences, 43 (8): 24—35. 
Williams, P., Couture N. and Blom, D. (2007) "Urban structure at Tiwanaku: geophysical 

investigations in the Andean Altiplano," in J. Wiseman and F. El-Baz (eds) Remote Sens- 
ing in Archaeology , Interdisciplinary Contributions to Archaeology book series, New York: 

Williamson, T (1998) "Questions of preservation and destruction," in P. Everson and 

T Williamson (eds), The Archaeology of Landscape: Studies Presented to Christopher Taylor, 

Manchester and New York: Manchester University Press. 
Williamson, R., Hurst, W. and Jeffe, M. (2002) "Satellite remote sensing for archaeology 

and historic preservation: mapping the ancient trails of Southeast Utah (ESA-SP515)," in 

B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage Conservation, 

Strasbourg: European Space Agency. 
Williamson, R.A. (1987) "Technology, preservation policy, and the National Park Service," The 

Public Historian, 9 (2): 118-24. 
Williamson, R.A. and Warren-Findley, J. (1991) "Technology transfer, historic preservation, and 

public policy," The Public Historian, 13 (3): 15—32. 
Wilson, C. (1976) Sinai, Jerusalem: Ariel Publishing House. 

Wilson, D.R. (2000) Air Photo Interpretation for Archaeologists , Stroud: History Press. 
Winterbottom, S. and Dawson, T (2003) "Islands of Coll and Tiree, the Inner Hebrides," 

A rchaeologia P 'o lona, 41: 287-8. 
Wiseman, J. (1998) "Insight: Eagle Eye at NASA," Archaeology, 5 1 (4). Online. Available HTTP: 

<> (accessed 8 September 2008). 
Wiseman, J. and El-Baz F. (eds) (2007) Remote Sensing in Archaeology, Interdisciplinary Contribu- 
tions to Archaeology book series, New York: Springer. 
Wiseman, J. and Zachos, K. (2003) "The Nikopolis Project: concept, aims, and organization," 

Hesperia Supplements , 32: 1-22. 
Wiseman, J.R. (1989) "Archaeology today: from the classroom to the field and elsewhere," 

American Journal of Archaeology, 93 (3): 437—44. 
Wiseman, J.R. (1996) "Wonders of radar imagery: glimpses of the ancient world from space," 

Archaeology, 49: 14-18. 



Witten, A J. (2005) Handbook of Geophysics and Archaeology ', London: Equinox Publishing. 

Wynn, J.C. (1986) "A review of geophysical methods used in archaeology," Geoarchaeology, 1 (3): 

Wynn, J.C. (1990) "Applications of high-resolution geophysical methods to archaeology," in 
N.P. Lasca and J. Donahue (eds) Archaeological Geology of North America, Boulder: Geological 
Society of America. 

Xiaohu, Z. (2004) "The preprocessing of hyperspectral remote sensing on Mausoleum 
Qin Shihuang," in C. Wang (ed.) International Conference on Remote Sensing Archaeology ', Beijing: 
Chinese Center for Remote Sensing Archaeology. 

Xin-Yuan, W, Ying-Qiu, Z., Yingcheng, L. and Chao, G. (2004) "On remote sensing archaeology 
analysis for transformation of traffic function of Sui Dynasty Tongji Canal," in C. Wang 
(ed.) International Conference on Remote Sensing Archaeology, Beijing: Chinese Center for Remote 
Sensing Archaeology. 

Yakam-Simen, R, Nezry, E. and Ewing, J. (1999) "A legendary lost city found in the Honduran 
Tropical Forest using ERS-2 and JERS-1 SAR imagery," Proceedings of the IGARSS 1999 
Geoscience and Remote Sensing Symposium, 5: 2578—80. 

Yoshimura, S., Kondo, J., Hasegawa, S., Sakata, T, Etaya, M., Nakagawa, T, Nishimoto, S., 
"A preliminary report of the general survey at Dahshur North, Egypt." Annual Report of the 
Collegium Mediterranistarum. Mediterraneus 20 , 3—24. 

Yugsi, R, Eisenbeiss, H., Remondino, R. and Winkler, W. (2006) "Multi-temporal monitoring 
of landslides in archaeological mountainous environments using optical imagery: the case of 
El-Tambo, Ecuador," in S. Campana and M. Forte (eds) From Space to Place: 2nd International 
Conference on Remote Sensing in Archaeology ■, Oxford: British Archaeology Reports. 

Yuqing, W, Kelong, T, Xiaohu, Z., Dewen, S., Qingbo, D. and Xinlong, N. (2004) "Discussion 
about the function of remotely sensed images in archaeology," in C. Wang (ed.) Interna- 
tional Conference on Remote Sensing Archaeology ', Beijing: Chinese Center for Remote Sensing 

Zhang, L. and Wu, J. (2006) "Remote sensing archaeology for ancient cities structure of Pingyao 
and Liandzhu, Yuhang City, Zhejiang Province, China," in S. Campana and M. Forte (eds) 
From Space to Place: 2nd International Conference on Remote Sensing in Archaeology ■, Oxford: British 
Archaeology Reports. 

Ziebart, M., Dare, P., Williams, T. and Herrmann, G. (2002) "Acquisition, registration, and 
application of Ikonos space imagery for the cultural world heritage site at Merv, Turkmenistan," 
in B. Warmbein (ed.) Proceedings of the Conference Space Applications for Heritage Conservation, 
Strasbourg: European Space Agency. 

Zubrow, E. (2007) "Remote sensing, fractals, and cultural landscapes: an ethnographic prole- 
gomenon using U2 imagery," in J. Wiseman and F. El-Baz (eds) Remote Sensing in Archaeology , 
Interdisciplinary Contributions to Archaeology book series, New York: Springer. 

Zubrow, E., Allen, K. and Green, S. (1990) Interpreting Space: The Use of GIS in Archaeology, 
London: Taylor and Francis. 

Zurawski, B. (1993) "Low altitude aerial photography in archaeological fieldwork: the case of 
Nubia," Archaeologia Polona, 31: 243—56. 



Page references followed by f indicate a figurative illustration, t indicates a table 

Abusir pyramid fields 165 

Acropolis, Athens 115f, 213 

"active" satellites 43 

Adams and Wood 25 

Adams et al. 25 

Aerial Archaeology 2 1 

aerial photography 3,7, 13-39, 46, 49f, 57, 

62, 77, 82t, 87, 94, 96, 110, 113, ll4t, 

128, 135, 152, 162, 175, 203, 206, 208, 

Aerial Reconnaissance Archives, Edinburgh 1 
Afghanistan 137f, 217, 231, 239 
Africa 47, 118, 234; fires 2002 51 
Agache, R. 19 
Agra 209 

Agredos Valley, Italy 69 
agriculture 139-40 
aircraft, 41, 77, 105, 153, 162, 236; 

ultralights 77, 239 see also balloons; 

helicopters; kites; parachutes 
Airphoto 56, 90 

AIRSAR, 78, 79, 149, 152, 153, 154 
Akasheh, T.S. 11,208 
Alabama, 55f, 103f, 107f 
Alamo, the Texas 37f 
Alcock and Cherry 173 
Alexandria 5, 127, 140, 170 
Alizadeh and Ur 162 
Alizadehet al. 160 
Allan and Richards 24 
alluvial plains 128-29, 203 
al-Raqqua, Syria 161 
Altai Mountains, Siberia 56 
Altaweel, M. 69, 99 
Amazon Basin 118 
Ambracian Gulf, Greece 138 

American Institute of Archaeology 32 
American Society for Photogrammetry and 

Remote Sensing (ASPRS) 221, 223, 231 
Americas, the 17, 18, 234 see also individual 

AmericaView program 34 
Amu Darya River 98 
Andes 62, 226 
Angkor Wat, Cambodia 10, 31, 61, 147, 

151-4, 152f, 153f, 154f, 186; Greater 

Angkor Project (GAP) 153 
animal habitats 236 
Antarctica 78 
anthropology 13, 176, 233 
Antiquity 5,15 
Apollo program 22 
Aquila, Italy 102 
ArcGIS 109, 192, 196 
Archaeologia 14 

Archaeological Institute of America 205, 231 
Archaeological Prospection 5 
Archaeology 32 

architectural features, identifying 198 
Arc View 56, 192 

Arenal region, Costa Rica 135, 136f 
Argon 19;KH-5 53, 54t 
Argotte-Espino and Chavez 134 
Arkansas River valley, USA 66 
Arlington National Cemetery 14 If 
Armenia 34 
Arpi, Italy 93 

Arroux River Valley, France 66, 96 
ARVI 92 
Ashur, Iraq 98 
Asia 118 
ASOR 32 



ASTER 41, 42t, 43, 67-70, 67t, 68f, 69f, 
76, 82t, 83, 85, 86, 91, 98, 99, 100, 110, 
H4t 117, 120, 121, 123, 124, 125, 126, 
128, 130, 137, 138, 141, 146, l48f, 162, 
164, 165, 166, 236; techniques for 
processing 82t 

Aswan 170; high dam 26, 212 

ATM (Airborne Thematic Mapper) 87, 126 

Auburn University, Alabama 103f 

Aufrecht, W.E. 173 

Australia 34, 152 

Austria 87 

automated archaeological site detection 



Babylon, Iraq 4f 

Badea et al. 140 

Bailey et al. 96 

Bajos 149-51 

Balaji et al. 134 

Ballard, R. 11 

balloons 13, 39 

Bamiyan Buddhas, Afghanistan 126, 

137f, 217 
band combinations 91—2 
Banks, E.P. 32 
Banning, E.B. 160 
Barasino and Helly 96 
Barbeau Creek Rock Shelter, North 

Carolina 18 
Barcelo et al. 3 
Barlindhaug et al. 213 
Barta and Bruna 165 
Barta et al. 165 

Bavarian State War Archives 1 5 
Beazeley, G.A. 15 
Beck, A. 50 
Becker, H. 82 

Becker and Fassbinder 81, 82 
Becketal. 139, 164 
Behrens and Sever 8, 31 
Beidha, Jordon 79 
Belgium 31 

Belize 24, 86f, 119, 150 
Belvedere et al. 106 
Ben-Dor, E. 132 
Ben-Dor et al. 79 
Beredes (Stubbs and McKees) 107 
Berry, J. 208 

Bertocci et al. 207 

Bevan and Conolly 108 

Bewley, M. 34 

Bewley and Musson 3 1 

Bewley and Raczkowski 8 

Bewley et al. 77 

Bezori et al. 34 

Bhattacharya, A. 138 

Bietak, M. 166 

Binford, L. 14 

Bintliff and Snodgrass 9 

Birecik Dam 161 

Birnbacher and Koudelka 219 

Blakely and Horton 173 

Blank, J.E. 21 

Blom, R.G. 31,39 

Blometal. 66, 109, 160 

Bloxam and Storemyr 137 

Blue Ridge Mountains, Shenandoah 

National Park 23, 24f 
Blumberg 75 
Blumberg et al. 75 
Boehler et al. 143 
Bogdanos, M. 217 
Bohemia 34 
Bolten et al. 165 
Boni, G. 14 

Boston, Massachusetts 35f 
Bourgeois and Marc 7 
Bradford, J.S.P. 18 
Brand, P. 165 
Brewer et al. 31, 167 
Brittany, France 77, 127 
Brivio et al. 129 
Bronze Age 79, 100, 108 
Brophy and Cowley 7 
B runner, U 161 

Bubenzer and Bolten 68, 123, 165, 199 
Bubenzer and Riemer 165 
Buettner-Januch, J. 18 
Bugayevskiy and Snyder 90 
burial mounds 56 
Burma 231 
Butzer, K. 130, 166 


Cagayun River, Philippines 102f 
Cahokia Mounds, Illinois 17f 
Cairo, Egypt 104, 108, 165, 229 
Calakmul Bioshere reserve, Mexico 93 
Cambridge, Massachusetts 121 



Camden, W. 17 

Campana, S. 98, 173 

Campana and Forte 8, 33, 115 

Campana and Francovich 203 

Campana and Frezza 229 

Campana et al. 34, 126 

Campbell, K.M. 23 

Canada, Space Agency 78 

canals 86, 91, 99, 105f, 109-10, 116, 119, 

128, 130, 134, 135, 139, 176, 177, 198, 

Candelaria, Mexico 119 
Canopic Channel 140 
Cantral, R.D. 20 
Capper, J. E. 14 
Carnac, Brittany 76 
Carnegie Institution of Washington 17 
Carr and Turner 31, 96 
Carter, H. 155 
Cashman, J. 167 
CASI 103, 126, 146 
CastlefordJ. 106 
CATA 126 

catacombs, Alexandria 4 
Caton-Thompson, G. 15 
Celone Valley, Italy 21, 22f 
Central America 17, 20, 33, 45, 96, 103, 

118, 148, 150, 151, 154, 184, 187, 189, 

211, 228 see also individual sites see also 

individual sites 
Cerasetti and Massimo 212 
ceremonial 143—4 
Chaco Canyon, New Mexico 2f, 17, 30; 

Chetro Ketl 20, 2 If; Poverty Point 27 
Challenger mission 74 
Challis, K. 77, 106 
Challis and Howard 77 
Challis etal. 77, 161 
Changlin and Ning 93 
Chapman, H. 3 
Chapoulie et al. 210 
Chevallier et al. 20 
China 32-3, 33f, 34f, 55, 92, 93f, 97f, 133, 

152, 211 see also individual sites 
Chittick, N. 211 
Chlodnicki et al. 167 
Christian Science Monitor 1 5 
Clapp,N. 158 
Clark et al. 65 

classification 94-6, 118t, 120, 123, 141, 199 
Cleave, R.W.L. 27 

climate change 162, 234, 241 

Cline, E. 227 

clouds 36, 38, 67, 69, 187 

coastal areas 126-7 

Cold War 19, 54, 55 

Cole et al. 12 

Coleman et al. 11 

combined techniques 109-10 

Comer, D. 78 

Comer and Blom 78, 105 

Comfort, A. 57,91, 162,212 

Comfort and Ergec 162 

Comfort et al. 162 

commercialisation 228 

Conant, F. 31 

Condit, H.H. 20 

Connah and Jones 34 

Conolly and Lake 3 

Conroy et al. 50 

conservation and heritage management 1 1 , 

Conyers, L. 81 
Cooper, F. 30 
Cooper et al. 96 
Core, P. 98 
coring I45f, 190 
Corinthia 87 
Corona 19, 72, 86, 88, 89f, 90, 98, 99, lOOf, 

109, 110, H4t, 126, 128, 130, 133, 139, 

140, 161, 162, 164, 166, 168, 201, 212; 

KH-1, KH-2, KH-3, KH-4, KH-4A, 

KH-4B 53t, 87, 54; KH-4B 56; 

KH-7/KH-9 41, 42t, 51-7, 53t, 56f; 

"strips" 54f; techniques for processing 82t 

Costa Rica 27, 30, 78, 135, 136f 
costs 36, 38, 42t, 52, 53t, 56, 57, 64, 66, 71, 

76, 77, 78, 87, 164-5, 204, 238 
Coutellier and Stanley 166 
Couzy, A. 234 
Cox, C. 31,60, 128 
Craig and Aldenderfer 65 
Crashaw, A. 18 
Crawford, O.G.S. 15 
Crawford and Keillor 17 
Crimea 99, 139 
Croatia, North 98f 
crop marks 16-17, 22f, 48, 4% 76, 121, 

123, 129, 132, 140, 171f, 175, 182-3, 

183f, 197, 198f, 211 



"Culture 2000-European Landscapes" 

project, English Heritage 32 
Cumbria, UK 30 
Custer et al. 27, 96 

dams 26, 55, 129, 135, 161,212 

Danebury, UK 65 

Darnell,). 215 

data: published 195, 230, 232; quality 78-9; 
sharing 230 

date of visit, importance of 194 

Davenport, G. C. 234 

Davidson and Shackley 170 

Davis, J.L. 29 

Dayalan, D. 210 

Deegan and Foard 34 

De Laet et al. 110 

Delaware, USA 27, 96 

Delta sites, Egypt 16, 49, 50f 56, 71, 75, 96, 
130, 147, 165-72, 174f, 195f, 215; East 
Delta 50f, 66f, 87f, 8% 129, 146, 166, 
167, 170, 183f, 188f, 189f, 191f, 192, 
237; northeast Delta 6f, 10, 92, 142, 188; 
West Delta l40f 

De Maeyer et al. 218 

Dengfeng and Dengrong 87 

Deo and Joglekar 3 1 

Deroin and Verger 213 

Deroin et al. 138 

desert 12, 13, 16, 17, 22, 25, 27, 28, 29, 
6lf, 62f, 68, 75, 94, 98, 121-3, 124f, 
165, 183, 213, 215 ; desertification 12, 

detection, amount of satellite 144—6 

Deuel, L. 7 

digital elevation models (DEMS), 56, 65, 67, 
68,70, 103, 137, 139, 165,234 

Digital Globe 72, 164, 236, 239 

digital photographs 38, 194, 200 

Dingwall et al. 213 

Discovery Channel 241 

Dolphin et al. 82 

Doneus, M. 34,87,213 

Doneus and Briese 106, 213 

Donoghue and Galiastagos 58 

Donoghue and Shennan 27 

Donoghue et al. 55, 164 

Dorsettetal. 27, 109 

Drager, D.L. 23 

Dubois, J.M.M. 31 

Earlybird 135 

Earth Resources Technologies (ERTs) 22 

Easter Island 3, 142, l43f 

Ebert, J.I. 21 

Ebert and Gutierrez 23 

Ebert and Lyons 86 

Ebert et al. 21, 23 

Economist, The 241 

ecotourism 207, 213 

Ecuador 100 

Edeine, B. 18 

education, role of 206, 213, 218, 219-20, 
240, 241 

Edwards, A. 166 

Edwards and Partridge 2 1 

Egypt 22, 34, 55, 66f, 68 76f, 86, 87, 96, 
99, 130, 132, 137, 139, l45f, 146, 147, 
160, 214-15, 225; Egyptian Antiquities 
Information Service (EAIS) 167; Egyptian 
Exploration Society, Delta Survey 1 67 ; 
Middle Egypt 26f, 38, 87, 96, 99, 130, 
I45f, 146, 165-71, l67f, l68f, l69f, 
171f, 175f, 176, 177f, 178f, 179, 180, 
181f, 182f, 185f, 192, 193f, 201f, 2l6f, 
225; Supreme Council for Antiquities 
(SCA) 167, 177, 182, 186, 220f see also 
individual sites 

Eichhorn et al. 123 

Eisenbeiss and Zhang 38 

Eisenbeiss et al. 38 

Elachi, C. 75 

el-Ashmunein 168, l69f 

El-Baz, F. 25-6,39,215 

El-Baz et al. 104 

El-Etr et al. 22 

El-Gamily et al. 215 

El-Ghandour, M. 167 

El-Nino 51 

El-Raeyetal. 215 

El-Rakaiby et al. 22 

El-Salvador 34 

Ellis Island, New York City 23f 

Emberling, G. 162 

Empereur, J. 5 

Endeavor shuttle 75 

Engelbach, R. 15, 16 

ENVI 84, 85 


EO-Hyperion 33 ; EO-1 Hyperion 91 

EOS 53 

ERDAS 84, 85 



ER Mapper (Earth Mapper) 84, 

Ernenwein and Kvamme 8 1 

EROS Data Center 53 

ERS-1 75 

ERS-2 75, 103 

Etaya et al. 104 


ethics 220-32; code of ethics 221, 230 ; legal 

considerations 223 
Europe 1, 18, 27, 33, 96, 120, 139, 213, 

218, 234 see also individual sites 
European Space Agency 27 
Evans and Farr 71, 100 
Evans et al. 3,79, 186 

Failmezger, V. 80 

Falk, O. 15 

Far East 18 

Farley et al. 66 

Fassbinder and Hetu 34 

feature types 131 Fedick and Ford 25 

Fedick et al. 25 

Feng Shui 143 

Ferrarese et al. 100 

Fiennes, R. 31 

filtering 102-3, 118t, 121, 140, 160 

Fisher and Fisher 158 

Fletcher and Evans 152, 153 

Folan et al. 25 

Foley, B. 11 

forests 105 121, 203; deforestation 109, 211, 

234, see also rainforests 
Forte, M. 94, 106 
Fort Resolution, New Zealand 108 
Fowler, M. 53, 58, 62, 73, 91, 96, 142 
Fowler and Curtis 58 

Fowler and Darling 75 Fowler and Fowler 53 
Fowler et al. 34 

France 22, 33, 34, see also SPOT 
Franco, L. 126 
French, C. 170 
Frihy et al. 215 
frozen/glaciated areas 130; frostmarks 140 

Garbuzov, G.P. 98 

Ganzin and Mulama 93 

Gemini 25 

Geomatica 85 

geophysics 3, 8, 81, georeferencing 56-7, 

72, 76, 82t, 87, 88-90, 89f, 164 

Germany 31, 75, 209; Space Agency 75 

Giddy and Jeffreys 82, 166 

Gillion, D.A. 20 

GIS 3, 8, 28-9, 32, 33, 36, 38, 46, 51, 
57, 69, 96, 100, 176, 208, 213, 218, 
221, 230, 233, 234, 236; compared to 
remote sensing 106-9, 107f, 117, 130, 
136, 137, 144, 149, 234; imagery 
processing 84, 85 

Given et al. 173 

Giza Pyramids, Egypt llf, 47f, 205 

GLIS 53 

globalization 204, 205, 232 

global warming 130, 131, 190, 204, 205, 

Glueck, N. 15, 183 

Gobi desert, Mongolia 75 

Gokhale and Bapat 71, 100 

Godja, M. 34 

Going, C. 18 

Golconda Fort, Hyderabad 134 

Goodman et al. 81 

Google Earth 7, 7f, 41, 42t, 44-50, 56, 80, 
88, 125, 129, 132, 166, 186, 205-6, 218, 
219, 222, 223, 224, 242; comparison with 
World Wind 51; Pro 46, 47 

Goossens and Van Ranst 96, 215 

Goossens et al., 56, 57 

GORS 32 

Gould, R. 1 


GPS 4, 56, 77, 95, 128, 164, 171f, 184, 186, 
190, 192, 195 197, 199, 230, 234 

grassland 123—4 

Great Wall of China, 97f, 205 

Greece 1, 66, 92, 96, 160 see also individual 

Green, N. E. 18 

Gregoire 95 

Gron, O. 211 

Gron et al. 73 

ground surveys 6, 8, 11 

Gumerman and Lyons 20 

Gumerman and Neely 2 1 

Gumerman and Schaber 20 

Gunn et al. 211 

Gupta et al. 106 

Guy, M. 31,65 

Hadjimitsis et al. 218 

Hadrian's Wall, northern England 7f 



Hafner and Rai 47 
Hagerman and Bennet 100 
Hailey, T.I. 38 
Hakobyan and Palmer 34 
Halkon, P. 34 
Hamilton, D. 8 
Hamlin, C.L. 22 
Handwerk, B. 205,241 
Hardy, D.D. 23 
Harp, E.J. 19,21 
Harrower, M. 139 
Hassan, F.A. 166 
helicopters 38 
Helly et al. 96 
Herbich, T. 81, 165 
Herodotus 209 
Herrera et al. 117 
Hieropolis, Turkey 103 
Hijazi and Qudah 65 
hilly areas 125 
Hirataet al. 113 
Hisar, Turkey 110 
Hixson, D.R. 117 
Hoffman, M. A. 23,73 
Hohokam culture 109 
Holcomb, D. 75,218 
Holcomb and Shingiray 75 
Holden et al. 77 
HolladayJ.S. 113 
Holleyetal. 17 
Holmul River, Guatemala 228 
Holocene landscapes 165 
Horns, Syria 10 147, 160-3, l6lf, l63f 
Hritz and Wilkinson 160 
Huadong and Changlin 32 
Huadong et al. 32, 33 
Hungary 3 1 
Hunt and Lipo 3, 143 
Hurcom and Harrison 92 
hype 227 

hyperspectral images: definition 43; studies 

infrared photography (IR) 18-21, 23, 36, 
43, 59t, 61, 64, 65, 118, 135, 140, 157, 
164; near-infrared (NIR) 36, short wave 
(SWIR) 67 1, 69,; thermal (TIR) 60t, 67; 
visible and near (VNIR) 67, 68, 78, 99 

InSAR 70 

installation 138 

Iraq 15,45,69, 160,217,219,231 see also 
individual sites 

Iron Age 209, 213; Celtic 96; 
Iron-Age-Age-Early Hellenistic 
periods 72 

IRS 134;IRS-1C31 

Ishwaran and Stone 219 

Ismu al-Arus, Egypt 2l6f 

Israel 34, 53, 79, 223 

Italy 32, 33, 34, 93, 100; Space Agency 69, 
74 see also individual sites 

Itanos, Greece 103, 126 

lure Vetere, Italy 93 

Jacob et al. 192 

James, J. 152 

Japan 217 

JARIC-National Image Exploitation Centre, 

Brampton 18 
Jeffreys, D. 165 
Jeffreys and Tavares 165 
Jennings and Craig 100 
JensonJ. 6,58,91,94,95 
JERS-1 75 
Jerusalem l6f 
Jiangsu province, China 101 
Johnson, A. 90, 236 
Johnson, J. K. 8, 106 
Johnson and Piatt 17 
Jokha, Iraq217f 
Jomard, E.F. 166 

Jordan 34, 78, 183; Transjordan 15 
Jorde and Bertram 2 1 
Journal of Field Archaeology 5 

Idelu Lagoon, Egypt 140 

IKONOS 31, 41, 42t, 59, 69, 72, 73, 88, 
99, 103, 110, ll4t, 117, 121, 123, 124, 
133, 135, 140, 143, 149, 150, 157, 162, 
164, 203, 209, 210, 218, 227, 228, 236, 
238; techniques for processing 82t 

imagery processing 84—5 

India 16, 31, 46, 100, 160; Southeast 210f 

Kamei and Nakagoshi 133 
Kardulias, L.P.N. 173 
Karnak Temple, Egypt 207f 
Karnieli et al. 123 
Kelong et al. 156 
Kelsey, F. 166 
Kemp, B. 166, 168, 176 
Kemp and Garfi 165, 225 



Kennedy, A. 1 5 

Kennedy, D. 15,34,56 

Kennedy et al. 35 

Kenyon,J.L. 3 

Kessler, D. 165 

Kharga Oasis 15, 29f, 199f 

Khawaga, M. 22 

KH imagery see Argon; Corona; Lanyard 

Khirbet Iskander, Jordan 108 

Kidder, C. 17 

Kidder and Saucier 30, 144 

Kill Site, Montana 23 5f 

kites 13, 38, 39 

Kligman, D.M. 206 

Knapp, A.B. 116, 173 

Knisely-Marpole, R. 39 

Kom el-Ahmar, Egypt 20 If 

Kouchoukos, N. 93 

Krishnamoorthy et al. 210 

Kruckman, L. 234 

Kumatsu et al. 100 

Kuwait 217 

Kvamme, K.L. 81,82 

KVR-1000 42t, 58, 91, 143, 161 

Kyoto Accord 218 

Ladefoged et al. 108 

Lagan River, Norway 97 

Lake Dalmaj, Iraq 130 

Lake Nasser 26 

Lake Peten Itza, Guatemala 45f, 46f, 68f, 
69f, 95f 

Lake Qatina, Syria 161 

Lambers et al. 108 

Lambert, D.P. 135 

Lambin et al. 123 

land reclamation 26f 

Land Remote Sensing Policy Act, USA 223 

landscape types ll4t, 117, 131, 118t, 190 

land use land cover changes (LUCC) 99 

Landis, K. 90 

Landsat program 21-4, 23f, 25f, 27, 28f, 
30f, 31, 38f, 41, 42t, 45f, 46f, 47, 51, 52, 
55, 58-63, 59f, 60t, 66, 80, 82f, 82t, 83, 
86f, 88, 90, 91f, 92, 93f, 95f, 97, 98f, 99, 
100, 104, ll4t, 119, 120, 123, 124f, 125, 
126, 127f, 128, 130, 132, 133, 134, 135, 
137, 138, 139, 140, 141, 143, 144, l45f, 
146, I48f, I49f, 150, 152f, 155, 159f, 
160, I6lf, 162, 165, 166, 167, 168, l69f, 
190, 201, 210f, 212, 213, 2l4f; ETM 60, 

228; MSS 96, 109, 215; pseudocolor 
151f, 153f, 158f, I63f; techniques for 
processing 82t; TM 92, 96, 103, 108, 
109, 121, 123, 149, 158, 161, 228 

Languedoc, France 100 

Lanyard 19; KH-6 53, 54t 

Lasaponara and Masini 94 

laser scanning 3, 108 

Lasithi, Greece 92 

Lawrence et al. 215 

legal considerations 223 

Leithia, Austria 105 

Leksand, Dalecarlia, Sweden 27 

Lemmens et al. 34 

Lenney et al. 215 

Lepsius, K. 168 

Leucci, G. 81 

Leviah Enclosure, Israel 78 

Lewis and Clark 90 

Libya 24, 25f, 27, 109, 170 

LIDAR 27, 35, 41, 76-7, 103, 104, 105-6, 
105f, 114, 119, 121, 126, 134, 140, 146, 

Lietal. 33, 111 

Lillesand et al. 5, 18, 59, 90, 91, 97, 98, 103 

Lima, Peru 7 If 

Limp, W. 28, 61, 106, 109, 170 

Lindbergh, C. 17, 149 

linking 134-6 

Little Colorado River, Arizona 20 

Liu et al. 32 

looting 11, 45, 48, 151, 156, 165, 184, 186, 
204, 206, 211, 215, 217f, 221, 224, 225, 
228, 229, 230, 232, 237, 239, 240f 

Lorenzo, H. 81 

Lorralde 29 

Loughcrew, Ireland 77 

Lunden, B. 27 

Luxor 16, 49, 75, 165, 209f, 225 

Lynott and Wylie 223 

Lyons et al. 21 

McAdams, R. 160 
McCauley et al. 75 
McGovern, P. 30 
McHugh et al. 27,75, 122 
McKinley, A.C. 17 
McManus et al. 87 
Machu Picchu, Peru, lOlf, 205 
macro-landscape studies 8 
Madagascar 66 



Madry and Crumley 29, 66, 96 

magnetometry 81, 83 

mali 123 

Mallorca, Spain 61, 90 

Mansourah, Egypt lOOf 

Manuas, Brazil 94f 

mapping, 8, 21, 27, 53, 149, 236 

Marcolongo and Bonacossi 8 1 

Marcolongo et al. 69 

Martinez-Navarrete, M.I. 138 

Martini and Souza 211 

Masini and Lasaponara 94 

Master and Woldai 162 

material culture 5, 8, 9, 10, 36, 98, 101, 

125, 137, 157, 162, 164, 168, 170, 173, 

180, 186, 187, 190, 194, 200-1, 202, 

Mathys, T. 53 
Mayan sites, Guatemala 3, 107, 135, 147-8, 

150, 189, 197,234 
Meats, C. 81 
Meats and Tite 8 1 
media attention 3 1 
Medinet Habu, Luxor 9f 
Mendes, Egypt 63, 64f, 142, 237 
Menze et al. 72, 139 
Merola et al. 93 
Merv, Turkmenistan 2 1 9f 
Mesopotamia 129, 130, 160 
micro-landscape studies 8, 188 
microwave imagery 20 
Middle East 15, 18, 33, 53, 71, 96, 139, 147, 

160, 161, 162, 165, 191, 194, 212, 231 
military photographs, role of 14—15, 18, 19, 

Mille Lacs Lake, Minnesota 82f 
mining 62, 137 
Minoan period 92 
Mindell, D. 11 
MI VIS 102 
Mohenjodaro, Pakistan 133f 
Moore, E. 152 
Moore and Freeman 152 
Moore et al. 154 
Mongolia 100, 124 
Monte Issi, Italy 93 
Mont St. Michel 2 13,2 I4f 
Montufu, A., 96 
Morozova, G.S. 130 
Morton Fen, Lincolnshire UK 27, 28f 

mortuary 14 1-2, 

Moshier and El-Kalani 88 

Mosul, Iraq 99 

Moundville, Alabama 237f 

Mt. Ararat, Turkey 227f 

Mt. Desert Island, Maine 6lf 

Mt Katahdin, Maine 125f 

Mt. Ruapehu volcano, New Zealand 104f 

mountainous landscapes 125 

Moussa, A. 22 

Mughan Steppes, Iran 162 

Multispec 90 

multispectral imagery, uses of 5 1 

Mumford, G. 57, 166, 225 

Mumford and Parcak 62, 97, 133, 199 

Mumford et al. 165 

Murghab River Delta region, 

Turkmenistan 212 
MVIS 93 
Myers, J. W. 28 

Napoleonic Survey of Egypt 166 
NASA 13, 20, 26-7, 32, 50, 67, 6% 78, 

152; "blue earth" 51; JLP 74, 99, 158; 

Space Act Agreement 148 see also Landsat; 

World Wind 
Nashef, K. 217 

National Defense Authorization Act 223 
National Geographic 26, 205, 219, 241 
natural disasters 208, 
Near East 1 60, 164 
Negev 15 

Neolithic period 27,160,161,212 
"New Archaeology" 14 
New Orleans, USA 105f, 208, 241 
New Scientist 29, 31 
Niknami, K.A. 136 
Nile, 75, 122, 130, 146, 166, 168, 170, 212, 

Ninevah, Iraq 98 
Nippur, Iraq 130 
normalized difference vegetation index 

(NDVI) 92-3, 97, 108, 118t, 123, 124, 

126, 127, 138, 164 
North Korea 231 
North Pole, aurora 5 1 
Norway 73, 134,211 

Okayasu et al. 124 
Okinetal. 102, 123 



Olduvai Gorge, Tanzania 234, 23 5f 

OMIS 101;OMIS2 156 

Orton, C. 173 

Ostia Antiqua 14 

Ostir and Nuninger 100 

Ostir et al. 93 

Owen, G. 38 

Ozarks 28 

paleohydrology 100 

Palestine 15, 27, 1 60 

palm desk accessory (PDA) 192, 204, 223 

Palmer, R. 21, 31, 62, 126, 218 

parachutes 38 

Parcak, S. 16, 33f, 55, 72, 87, 92, 96, 99, 
121, 129, 130, 132, 133, l45f, l67f, 168, 
16% 170, 171f, 174f, 177f, 178f, 179f, 
181f, 182f, 185f, 188f, 189f, 191f, 193f, 
196f, 201f, 215, 2l6f, 220f, 225 

Parker et al. 123 

Parry, J. 30,62 

Parssinen et al. 63, 119 

Parrington, M. 24 

"passive" satellites 43 

Pavlish, L. 82, 174f 

Pavlish et al. 82 

PCI Geomatica 84 

Peacock, D. 215 

Peebles, C.S. 8 

Persia 160 

Peru 17, 91f, 226 

Peten, Guatemala 10, 103, 147-51, l49f, 
15 If see also Lake Peten; Mayan sites 

Peter, N. 218 

Peterman, G. L. 108 

Petra, Jordan 15, 78, 126, 208 

Philip etal. 161 

Piovan et al. 100 

Piro and Capanna 173 

Plain of Jars, Laos 73 

Poidebard, O.G.S. 15 

Pompeii, Italy 14, 238f 

population increase 11, 55, 183 

Pottier, C. 153 

Posnansky 25 

Pouls et al. 20 

Powesland, D. 77 

Powesland et al. 126 

Poverty Point, USA 144, l45f 

principal components analysis (PCA) 97—9, 
101, 118t, 127, 128, 138, 141, 151, 164, 
167, 170 

Purana Qila, Hyderabad 134 

Pursell, C. 241 

Qingjiu and Jianqiu 101 

quarries 137, Quickbird 29f, 33, 36, 37f, 38, 
41, 42t, 48, 58, 64f, 71-3, 74f, 80, 83, 
88, 93, 98, 99, 103f, HO, ll4t, 117, 121, 
123, 126, 127f, 131, 132, 137, 138, 140, 
141, 142, 149, 157, 162, 164, 165, 168, 
171f, 190, 194, 198, 201f, 203, 210, 228, 

Rabeil et al. 106 

radar 24-5, 31, 43, 73, 75, 82t see also GPR; 

Shuttle Radar Topography Mission; 

RADAR 27, 31, 82t, 85, 104, 119, 129, 

130, 135, 152, l60jee*ZroSAR;SIR- A; 

RADARSAT 75, 78 104, 123, 130, 133, 138 
radiocarbon dating 135, 234 
rainfall 92, 117, 128 

rainforests, 45, 117-18, 148,; Brazil 119f 
Rajamanickam et al. 106 
Ramasamy et al. 210 
Rapp, G. 22, 86 
Rapp and Hill 170 
Rawson,J. 155 

recording sheets 192 193f; example 201—2 
Rees,L.W.B. 15, 16 
Reindel, M. 144 
religion 142 
Renfrew and Bahn 210 
reservoirs 138 
resistivity 51,77,81,83 
resource management 138, 139, 206, 236; 

cultural resource management 33, 79, 174 
Ricchetti, E. 103, 108 
Richardson and Hritz, 130 
Riley, D. 7, 34 
Rio Grande, Peru 144 
Risboletal. 134 
River Foss, North Yorkshire 76 
River Ouse, North Yorkshire 77 
rivers 8, 29f, 44, 65, 100, 129, 212; "radar 

rivers" 27, 75, 122 see also individual sites 
River Trent, English Midlands 77 
River Witham, Lincolnshire 77 



Rizzo et al. 82 

Roberts, A. 124 

Robinson, C. 123 

Romano and Schoenbruan 125 

Roman period 34, 123, 173, 188, 205, 237, ; 

divisions 61, 68; roads 34, 58 
Rome 1, 160; Forum 2f; Colosseum 120f 
Roosevelt, A.C. 81 
Rossi and Ikram 38 
Roughley et al. 77 
Rowlands and Sarris 126 
Rowlands et al. 103 
rural areas 121; transition urban/rural 

areas 121 
Russia 58, 98, 108, 226 

Sagalassens, Turkey 63 
Sahara 27 
Said, R. 166 
St. Joseph, J.K.S. 7, 19 
Salt River, Arizona 109 
San Bartolo 147-9 
San Born Company 208 
Sanmenxia Reservoir 32, 34f 
SAR 41, 66, 70, 78, 83, 85, 100, 103, 104, 
109, 119, 122, 138, 142, 150, 210, 236 
Sarris, A. 81 
Sarris and Jones 8 1 
Sarris et al. 81,92, 138 
Saturno et al. 3, 117, 147-9, 150, 189 
SAVI 92 

Saving Antiquities for Everyone (SAFE) 228 
Schiffer, M. 170 
Schipper, F. 165 
Schmidt, A. 3 
Schmidt and Karnieli 92 
Scientific American 241 
Scollar, I. 8, 19 
Scollar et al. 8 
Scotland 62, 213 
sea, sites 1 1 

seasonality 49, 87, 92, 113, 157, 188-9 
settlements 131-4 

Sever, T. 20, 26, 27, 39, 149, 150, 187 
Sever and Irwin 228 
Sever and Wagner 31, 96 
Sever and Wiseman 27 
Shang Dynasty 109 
Sharpe, P.H. 14 
Sheets, P. 27, 30, 39, 135 

Sheets and Sever 30, 31, 79, 96, 135 
Sheets and Wiseman 27 
Shell, C. 77, 140, 198 
Shell and Roughley 77 
shell mounds 101, 102f 
sherd scatters, 116, 181f, 201f 
Sherrat, A.G. 72, 139 
Showater, P. 63, 110 
Shupeng, C. 211 
Shuren, L. 211 

Shuttle Radar Topography Mission (SRTM) 
41, 42t, 70, 71f, 100, 101, 107, ll4t, 

121, 123, 125, 127, 128, 135, 137, 139, 
140, 150-1, 160; techniques for 
processing 82t 

Siberia 13 If 

Silk Road 122 

Sinai 76, 97, 186, 187; North Sinai lagoon 

system 88f 
Sintubin et al. 63 

SIR-A 27 31, 41, 42t, 73, 75, 76f, 158 
SIR-B27,4l,42t, 73, 158 
SIR-C 41, 42t, 70, 73, 75, 104, ll4t, 119, 

122, 129, 152, 153, 158, 160; techniques 
for processing 82t 

site: defining a site 116, 118t height 198-9; 

names 195; ownership 197; shape 197 
size 198; types ll4t 
Sittler and Schellberg 139 
Snow, E. 22 
Society for American Archaeology (SAA) 32, 

Soetens et al. 144 
Soghor, C. 63 

soils 5, 68, 69, 74, 97, 105, 170, 191 
Som, N. 106, 133 
Sonneman et al. 152 
Soreide, F. 11 

South America 33, 78 see also individual sites 
Southeast Asia 151, 187, 241 
Southern California, 38f 
Soviet Union 18 
Space Imaging (GeoEye) 73 
space technologies, development of 18— 19, 

22, 26, 32, 33, 69, 74, 77 see also Corona; 
Landsat; NASA 
Spain, 61, 78 
Spencer, J. 168 
Spin-2 83 
SPOT 22, 31, 41, 42t, 47, 65, 65t, 66f, 82t, 

84, 85, 86, 91, 92, 98, 100, 103, 104, 



109, HO, H4t, 120, 121, 123, 125, 128, 
129, 130, 132, 133, 134, 137, 138, 141, 
143, 144, 146, 161, 166, 190, 208, 212, 
213, 215; techniques for processing 82t 

Stafford etal. 98, 138 

STAR-3-DEM 228 


Steadman, S. 170 

Stein, A. 15 

Stein and Cullen 138 

Stern and Salam 7 5 

Stewart, D. 108 

Stichelbaut, B. 14 

Stine and Decker 106 

Stone, E. 219, 228 

Stonehenge, UK 14, 15f, 58, 62, 63f, 96 

Stubbs and McKees 107, 108 

Sudan 212 

Sultan et al. 215 

Summers, G. 39 

Supajanya, T 139 

Survey and excavation Projects, Egypt 

Switzerland 32, 217 

Sykes, L. 122 

Symanzik et al. 106 

Syria 27, 32, 34, 55, 86—7 see also individual 

Taj Mahal, India 205, 206f, 209 

Takwa, Kenya 212f 

Tamil Nadu, India 21 Of 

Tan et al. 32 

Tang Mausoleum, China 143 

Tanis, Egypt 189, 198f 

Tannehill, Alabama 107f 

Tartaglia, L.G. 21 

Tartaron, T.F. 39 

Taylor, P. 227 

Tehuacan Valley, Mexico 2 1 

Tell ed-Da'ba, Egypt 183f 

Tell-el-Amarna, Egypt 10, 16, 38, 74f 132, 

147, 166, 168, 225f 
Tell Ras Budran, South Sinai 49, 50 
Tell Sariri, Delta 129f 
Tell Sharufa, Egypt 189f 
Tell Sheikh Masara, Egypt 17 If, 201 
Tell Tebilla, Egypt 6f, 57, 59f 166, 225 
Thailand 30, 46, 62, 237 
Thakker, PS. 132 
Thakran, R.C. 16 

theft see looting 

Thematic Mapper simulators 30 see also ATM 

thermal imagery 3, 42t, 43, 59t, 60, 93, 

117, 130, 142, 197; thermal inertia 87, 

156 see also ASTER; Landsat ETM; 

Thomas, B. 158 
Thompson, H. 20 3D 43 46, 48 51, 56, 

67,70,77, 100, 115, 125, 135, 152, 

157, 199,236 
thresholding 97, 118t 
Tieszen et al. 93 
TIFF images 47 
Tikal, Guatemala l48f, 150f 
timing 11, 194 
Timor, G. 108 
Timothy Dwight College, 

Yale University 48f 
TIMS 27, 30, 35, 135, 149 

Tongwangcheng, China 93 
topography, 46, 100 
Toprak, V. 126 

tourism 213—14; ecotourism 207, 213 
transitory/fringe areas 136—8 
Traviglia, A. 102 
Trelogan, J. 99 
Trelogan et al. 139 
Trinity College, Cambridge 49f 
Tuna el-Gebel, Middle Egypt 143, l44f 
Tundra 130 

Turkey 63, 91, 160, 161 
Tuscany 98, 203, 204 
Tykot, R.H. 31 

Ubar, Oman 10, 31, 147, 158-60, 159f 
UK 18, 21, 23, 27, 30, 34, 61, 76, 106, 

139, see also individual sites 
Umma, Iraq 240f 
Umm el-Qaab, Egypt 200 
UNESCO 27, 32, 156, 210, 212, 218, 219 
Universal Transverse Mercator (UTM) 90 
Ur,J. 55,87 
urban development 11, 215, 225 ; transition 

areas 121, 183; urban surveying 179f 
Urban STAR-3 149 
Ureturituri Pa, New Zealand 108 
USA 17, 18, 22, 23, 27, 34, 46, 51, 52, 

70, 90, 96, 120, 223, 229, 233, 237; 



DoD 79, 105, 217, 231; Native American 
focus 23 see also individual sites and 

USAID218, 226 

USGS52, 55, 56f, 70 

Usumacinta River, Mexico/Guatemala 107 

Utah, USA 135 

Utzi 131 

Vale of Pickering, UK 77 

Valley of the Kings, Egypt 142, 208, 209f 

Van Andel, T.H. 5 

Van den Brink, E. 166,215 

Vanua Levu, Fiji 127f 

vegetation, types of 174 see also normalized 

difference vegetation index 
Venice 14 

Verhoven and Loenders 39 
Vercoutter, J. 34 
Vermuelen, F. 137 
Vietnam War 73 
Vining and Wiseman 138 
virtual reality 3, 1 14 see also 3D 

war 217 see also World War 

Wari Empire, Peru 100 

Washington, DC l4f 

waterlogged areas 128 

water management strategies 34 

water sources 8, 59, 62, 122, 123, 199, 

Watson, R. A. 21 

Weis, H. 34 

Weller, E. 150, 151 

Welzenbach, M. 158 

Wendorf, F 27 

Wendorfetal.27, 199 

Western Desert, Egypt 25, 75, 104, 122, 

123, 124f, 165,213 
Western Karakum Desert, Central Asia 98 
Western Peloponnesus, Greece 3 Of 
Wheeler, M. 15 

White Sands Proving Ground 19f 
Wiegend, T.15 

Wilkinson, TJ. 3, 139, 160, 162, 166 
Wilkinson et al. 139, 160, 164 

Williams etal. 81, 108 

Williamson, R.A. 229 

Williamson, T. 207 

Williamson and Warren-Findley 144 

Williamson et al. 135 

Willey, G.R. 25, 118 

Wilson, C. 187 

Wilson, D.R. 3 

Winterbottom and Dawson 62 

Wiseman, J. 26, 28, 32, 39 

Wiseman and El-Baz 8, 22 

Wiseman and Zachos 66 

Witten, A J. 3 

World Archaeology Conference 231 

World Monument Fund 139, 152 

World War I 14 

World War II 18,212 

World Wind 7, 37f, 41, 42t, 51 88, 127, 

129, 132,224 
WynnJ.C 81 

Xi'an, China 10, 147, 155-8, 155f, 157f, 

Xiaohu, Z. 101, 156 

XiXi shell mound, Philippines 101, 102f 
X-SAR4l,70, 73,75,94f, ll4t, 153; 

techniques for processing 82 1 
Xucutaco-Hueitapalan, Mexico 103 

Yakam-Simen et al. 103 

Yaxha 150 

Yellow River plain, China 66, 109 

Yemen 137, 161 

Yoshimura et al. 165 

Yucatan Peninsula, Mexico 93, 

Yugsi et al. 100 

Yuqing et al. 97 

Zhang and Wu 34 
Zhouyuan, China 92 
Ziebart et al. 218 
Zimbabwe 15, 23 If 
Zubrow, E. 1 
Zubrow et al. 8 
Zurawski, B. 34